spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T03:59:04.130Z","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T03:59:04.136Z","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}4{"parentUuid":null,"isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":"Begin"},"uuid":"707e0cc0-7149-47ac-be61-f6fe5c09bd7e","timestamp":"2026-10-30T03:59:04.140Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","shapeHash":"b8d809ac82417bf397835e681565a3bdc00d93faf0e3078518fb99abc4c3b760","timestamp":"2026-10-30T03:59:04.155Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RP0021\nDate: 2026-10-29\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}12{"parentUuid":"7be8734b-d3f3-4667-b782-3a64b49d1acd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkaAxAuwRgv4navUuXH","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":138,"output_tokens_details":{"thinking_tokens":22},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":449,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkaAfJwbEXXhwuzsRNz","requestRef":"57b3e2ea-6ffa-42cd-ae17-5316604f9815","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"76bfc0fb-8036-4354-ac0e-d07af8d77257","timestamp":"2026-10-30T03:59:06.776Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"76bfc0fb-8036-4354-ac0e-d07af8d77257","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkaAxAuwRgv4navUuXH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QJ2btzZkCzzmMiPCwE6Pyb","name":"Bash","input":{"command":"cat TASK.md; 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The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:36 .\ndrwxr-xr-x  17 simon-pierreboucher  staff   544 oct.  29 23:59 ..\n-rw-r--r--   1 simon-pierreboucher  staff   508 oct.  29 23:36 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:33 data\n-rw-r--r--   1 simon-pierreboucher  staff  2093 oct.  29 23:34 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4893 oct.  29 23:34 TASK.md\ndata:\ntotal 40\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:33 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:36 ..\n-rw-r--r--  1 simon-pierreboucher  staff   1174 oct.  29 23:33 README.md\n-rw-r--r--  1 simon-pierreboucher  staff  14211 oct.  29 23:33 UNRATE.csv","is_error":true,"tool_use_id":"toolu_01QJ2btzZkCzzmMiPCwE6Pyb"}]},"uuid":"a0ca8281-366e-42d0-858b-1d286a5c7998","timestamp":"2026-10-30T03:59:07.551Z","toolUseResult":"Error: Exit code 1\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: can a simple time-series model beat the naive forecast?\n\n## Scientific question\nOver the last three years, does a simple autoregressive-type model forecast the US civilian unemployment rate better than the naive (no-change) forecast at one- and three-month horizons? The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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Source: U.S. Bureau of Labor Statistics, Current Population Survey, via FRED (Federal Reserve Bank of St. Louis).\n- **Retrieval URL**: https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE\n- **Retrieved (UTC)**: 2026-10-30T03:20:36Z\n- **SHA-256**: 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\n- **Format**: exactly as served by FRED: header `observation_date,UNRATE`, one row per month dated on the first day of the month (YYYY-MM-01), 945 data rows from 1948-01-01 to 2026-09-01. Missing observations are served as an empty value field.\n- **Units**: percent of the civilian labour force.\n- **Vintage caveat**: this is the current (final) vintage as of the retrieval time. Values may have been revised since their first publication (annual seasonal-factor revisions). Rolling-origin results on this file therefore measure forecastability of the revised series, not historical real-time performance.\n- **License / terms**: FRED terms of use (https://fred.stlouisfed.org/legal/); BLS data are public domain.\n{\n  \"task_id\": \"F3-dev-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"model_name\": {\"type\": \"string\", \"nullable\": false},\n    \"model_description\": {\"type\": \"string\", \"nullable\": false},\n    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"n_evaluated_h1\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_evaluated_h3\": {\"type\": \"integer\", \"nullable\": false},\n    \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F3-dev-01\",\n  \"family\": \"F3\",\n  \"split\": \"dev\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:36:32.922288+00:00\",\n  \"files\": {\n    \"TASK.md\": \"833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351\",\n    \"data/README.md\": \"62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1\",\n    \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n    \"gate_spec.json\": \"0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012\"\n  }\n}observation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\n2025-10-01,\n2025-11-01,4.5\n2025-12-01,4.4\n2026-01-01,4.3\n2026-02-01,4.4\n2026-03-01,4.3\n2026-04-01,4.3\n2026-05-01,4.3\n2026-06-01,4.2\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n935:2025-10-01,\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     946 data/UNRATE.csv","is_error":false}]},"uuid":"92188e55-2e1a-4c0d-aa13-19cf28823b2f","timestamp":"2026-10-30T03:59:09.847Z","toolUseResult":{"stdout":"# data/README.md — F3-dev-01\n\n## UNRATE.csv\n- **Series**: UNRATE — Unemployment Rate, United States, percent, seasonally adjusted, monthly. Source: U.S. Bureau of Labor Statistics, Current Population Survey, via FRED (Federal Reserve Bank of St. Louis).\n- **Retrieval URL**: https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE\n- **Retrieved (UTC)**: 2026-10-30T03:20:36Z\n- **SHA-256**: 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\n- **Format**: exactly as served by FRED: header `observation_date,UNRATE`, one row per month dated on the first day of the month (YYYY-MM-01), 945 data rows from 1948-01-01 to 2026-09-01. Missing observations are served as an empty value field.\n- **Units**: percent of the civilian labour force.\n- **Vintage caveat**: this is the current (final) vintage as of the retrieval time. Values may have been revised since their first publication (annual seasonal-factor revisions). Rolling-origin results on this file therefore measure forecastability of the revised series, not historical real-time performance.\n- **License / terms**: FRED terms of use (https://fred.stlouisfed.org/legal/); BLS data are public domain.\n{\n  \"task_id\": \"F3-dev-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"model_name\": {\"type\": \"string\", \"nullable\": false},\n    \"model_description\": {\"type\": \"string\", \"nullable\": false},\n    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"n_evaluated_h1\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_evaluated_h3\": {\"type\": \"integer\", \"nullable\": false},\n    \"rmse_naive_h1\": 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — F3-dev-01\n**Question:** Over 36 rolling expanding-window origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast US UNRATE better than the naive no-change forecast at h=1 and h=3 months?\n**Estimands:** out-of-sample RMSE and MAE (percentage points) of naive and model per horizon over origins with non-missing actuals; relative improvement 1 − RMSE_model/RMSE_naive per horizon. Meaningful assuming the final-vintage series is the evaluation truth (not real-time) and that the 36-origin sample is a fair (small) sample of recent forecasting conditions.\n**Outputs:** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model), out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, n_evaluated_h1/h3, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/exclusions.csv.\n**Information rules:** forecast at origin t uses only obs dated ≤ t (estimation, model choice, transforms). Naive = latest available obs ≤ t. Missing actual → ledger row with empty actual, excluded from stats. No real-time claims.\n**Invalidity conditions:** use of data after t in fitting; model chosen by looking at evaluation-period errors; fewer origins than specified; fabricated values.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29: P0 passed (contract written).\" > log.md\ncd ../.. && ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.dtypes.to_dict(),d.observation_date.min(),d.observation_date.max())\nprint(d.UNRATE.isna().sum(), d.observation_date.duplicated().sum(), 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2) {'observation_date': dtype('<M8[ns]'), 'UNRATE': dtype('float64')} 1948-01-01 00:00:00 2026-09-01 00:00:00\n1 0 count    944.000000\nmean       5.652966\nstd        1.703534\nmin        2.500000\n25%        4.300000\n50%        5.450000\n75%        6.700000\nmax       14.800000\nName: UNRATE, dtype: float64\ngaps in dates 0\n    observation_date  UNRATE\n866       2020-03-01     4.4\n867       2020-04-01    14.8\n868       2020-05-01    13.2\n869       2020-06-01    11.0","is_error":false}]},"uuid":"5fdea7ab-37f4-450a-98ee-307079c3fa2e","timestamp":"2026-10-30T03:59:30.621Z","toolUseResult":{"stdout":"(945, 2) {'observation_date': dtype('<M8[ns]'), 'UNRATE': dtype('float64')} 1948-01-01 00:00:00 2026-09-01 00:00:00\n1 0 count    944.000000\nmean       5.652966\nstd        1.703534\nmin        2.500000\n25%        4.300000\n50%        5.450000\n75%        6.700000\nmax       14.800000\nName: UNRATE, dtype: float64\ngaps in dates 0\n    observation_date  UNRATE\n866       2020-03-01     4.4\n867       2020-04-01    14.8\n868       2020-05-01    13.2\n869       2020-06-01    11.0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1eb8171b-499e-45d0-9d6e-5d760def866e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"691f7ccf21ae597ef99970d4dd7cb4225710292556fbd3dbf77b82aec9903f99","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — F3-dev-01\n**Question:** Over 36 rolling expanding-window origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast US UNRATE better than the naive no-change forecast at h=1 and h=3 months?\n**Estimands:** out-of-sample RMSE and MAE (percentage points) of naive and model per horizon over origins with non-missing actuals; relative improvement 1 − RMSE_model/RMSE_naive per horizon. Meaningful assuming the final-vintage series is the evaluation truth (not real-time) and that the 36-origin sample is a fair (small) sample of recent forecasting conditions.\n**Outputs:** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model), out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, n_evaluated_h1/h3, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/exclusions.csv.\n**Information rules:** forecast at origin t uses only obs dated ≤ t (estimation, model choice, transforms). Naive = latest available obs ≤ t. Missing actual → ledger row with empty actual, excluded from stats. No real-time claims.\n**Invalidity conditions:** use of data after t in fitting; model chosen by looking at evaluation-period errors; fewer origins than specified; fabricated values.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29: P0 passed (contract written).\" > log.md\ncd ../.. && ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.dtypes.to_dict(),d.observation_date.min(),d.observation_date.max())\nprint(d.UNRATE.isna().sum(), d.observation_date.duplicated().sum(), 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out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/UNRATE.csv — SHA-256 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README and bundle.json).\n- 945 data rows + header; 2 columns: observation_date (YYYY-MM-01), UNRATE (percent, SA, float).\n- Date range 1948-01-01 … 2026-09-01; monthly grid complete (no missing dates), no duplicate dates.\n- Missing values: 1 (2025-10-01, empty field). Non-missing: 944. Range 2.5–14.8; max is 2020-04 (COVID spike, genuine, retained).\n- Implications of the missing month: origin 2025-10-01 has no own observation (naive uses 2025-09-01 value); target 2025-10-01 is missing for (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3) → those pairs kept in ledger with empty actual, excluded from stats. Model handles the gap via the Kalman filter (state-space likelihood skips the missing observation).\n- Availability: the file is the final vintage (retrieved 2026-10-30), so values may include revisions unavailable at origin dates. This cannot be avoided with the given input; handled by stating results are pseudo-out-of-sample on revised data, no real-time claim. Data-dating rule (≤ t) is enforced in code by truncating the series at each origin.\nEOF\ncat > log.md <<'EOF'\n- 2026-10-29: P0 passed (contract written).\n- 2026-10-29: P1 audit done; one missing value (2025-10). Decision: model = ARIMA(2,1,0) without constant (AR(2) on first differences), Gaussian MLE via Kalman filter (statsmodels SARIMAX), expanding window from 1948-01; chosen a priori, not tuned on evaluation errors.\nEOF","description":"Write data audit and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2375,"cache_read_input_tokens":8741,"output_tokens":991,"output_tokens_details":{"thinking_tokens":240},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2375},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkc1DqH9f7BxQbfLWWW","requestRef":"e2d58894-97a9-42f5-810e-af0d0337ba40","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4501c32c-3592-439f-ba84-8d766bfb2874","timestamp":"2026-10-30T03:59:41.022Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"4501c32c-3592-439f-ba84-8d766bfb2874","isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TDtofFJFbzwYJuTd2Q1hbM","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"1b6a9e5d-9f81-456c-8018-99c46fb0b45d","timestamp":"2026-10-30T03:59:41.062Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4501c32c-3592-439f-ba84-8d766bfb2874","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021/out/state","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"9edc8b78f8a47815a1b1cc3e948e4b693e2f4b785ac4d1dfd45521da60281ce9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS1goKEAgTGAI4AUIIdGhpbmtpbmcSDAJ1Wmmlj5Jb5CU7xxoMPcyMKMAEHgjQciVGIjCxm+C5D1Ou14T3+aXLRp51tLBfccy9+PSWvVouyslm04IKOQfW1o7aagNCA0FtjwYq8wmSHBnij5JyOgfMz7WveoROY44E0SgvPHSZqrtXce/tdOmsK+5fVdeyWOgxiK2DC+Alb7CXWb7JSiBskXA0jzeMX5RbFsWH3OvTijmuuf1gdciEcODzeorw87/bZwLA2squAPNZZ2cNzj0n8gBzkfB1j0Omn8hPR9llw1/uY8X/ZuEjXsCYhj4TPNGnAVTReSseaFmaW3tcxJD6H0HczcoTlqxGC1xK+6Ean7MNAHJ4Cxa13TgTCpKADwW8Br5cFvcYfxamzuuUJzen1CB65GilLFdWEA8Lsz0tC/wCe4Evgd3kUXFcB11PoikSigjU4cB7iydbBYY9XnGx0akPFursDOrcDaRFuEb/lSrquv8ArRXfx77nkGyBBv7Xzgc+jqSfplrQe1Am+jIVJGKNfJm+/CjW03eH7Lmna1jj6KJsfzfLm6xxlE/PsowEK/dKa9nPi4QMd4j1DVov7BUOIkIuv+Xn6NJ99odhaAl2Wgr6Iz+dkjZXzzxveLMKTD+TXZbIbqn0Lyri2ir9iQjcXLmsWNPhqwn6155wfVtMbruHSc8bq+9oMLPw3Yh7j76eQWtlzxD3tTCP1Y8+kDLDywxuqe08oFstrJg8qjW5mu5MYvm6pH3GJSmTjAHYA9e13dW5J/R563yKv2Fn3DwzYW8Ll2F5N6x+1EQL9SP1ZjTynugWOlJ2waRxPsSxMG4j9t7Cy7q0PuX/FJuHHNmx3AtdzD1hpbpfrqboHynOfAywBNdCQ96zfiDjrWXS5RdXE+WnwD62FUE82+rTG5l8OaETk5sBbOqc6kcPrPeHeOpx2vGsekFoTPhjKKzcRfSmcGFyCYiVp21yleqnqvmfBcPtK4unLdmB0To9FAanizJHtZBO24TwF50GqmD2ShF/Wlh6aes7kTzXT58Mwl9RfvECpySw3L6btL69fTmOYg5F0jakmOsFODBV1pY0Y6OWV9WDVTWwsgJX36szWtE+GYhoHYQYUVYS/RD9XN/sGpWTE4zifoK8hbGxqNcnJf8VLC/hMkwkeRWj7YvMNK3Cgkroq7Ugn/1S3ODQxcXb8VcQfEH3YctIG6e5y2Zf9W4T7kJN4ZAlygRwF595JLQu4Q4rPgQ2PnXVFkKqSYM+x8zhdKi8BD6suGOqVhB+T24hk6/GrnoG5imxm77zScRrzkgfi5slfokEGEad8x2/CtIXAkpckdUMOG59uZvPj8E06mGnHYZj1DMQam419wo8dp1Op7QyWG1vVmmTk1BNOm3uyqM60QNcHWCGqaMfy5fZe95tUMtRGhpcmxZYCmP78uOMekxkcAYYXFQUUI+VgMrA3AY98cmhq6zL5JN6zYhG14eajXT0W2amfNb/qjb+FsRMwjRTVsPHmQo+oKseMZpTZEM6MRQV7r/e8b0ZRKiH2/TX5gWhVcT9HLGI8BHakiKkqc5c36v9mbyRJx9/ABaPn8myg5jsqhQ0Gn27hXBvlfxf26qPR37q5833pj0cALEbfKbZ47//P4q6uCeACAIeMSankpnHhCsSVIwwaV339Abvno3OG23TaAeAPhAzsLMbsHedEuYu38YqziRmycXbKXAxjc8BATtCtasAkqxY7C9EZgnLYP23yLwnxcSABjSA1KG/PfMsxSK7mP+uUi3ZLUemcSNOG4JwfsOcXWrTPZDm8Pt3qxEB/vgREiDbmNvjEk/UGAE="},{"type":"tool_use","id":"toolu_01TDtofFJFbzwYJuTd2Q1hbM","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/UNRATE.csv — SHA-256 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README and bundle.json).\n- 945 data rows + header; 2 columns: observation_date (YYYY-MM-01), UNRATE (percent, SA, float).\n- Date range 1948-01-01 … 2026-09-01; monthly grid complete (no missing dates), no duplicate dates.\n- Missing values: 1 (2025-10-01, empty field). Non-missing: 944. Range 2.5–14.8; max is 2020-04 (COVID spike, genuine, retained).\n- Implications of the missing month: origin 2025-10-01 has no own observation (naive uses 2025-09-01 value); target 2025-10-01 is missing for (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3) → those pairs kept in ledger with empty actual, excluded from stats. Model handles the gap via the Kalman filter (state-space likelihood skips the missing observation).\n- Availability: the file is the final vintage (retrieved 2026-10-30), so values may include revisions unavailable at origin dates. This cannot be avoided with the given input; handled by stating results are pseudo-out-of-sample on revised data, no real-time claim. Data-dating rule (≤ t) is enforced in code by truncating the series at each origin.\nEOF\ncat > log.md <<'EOF'\n- 2026-10-29: P0 passed (contract written).\n- 2026-10-29: P1 audit done; one missing value (2025-10). Decision: model = ARIMA(2,1,0) without constant (AR(2) on first differences), Gaussian MLE via Kalman filter (statsmodels SARIMAX), expanding window from 1948-01; chosen a priori, not tuned on evaluation errors.\nEOF","description":"Write data audit and 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> out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin forecasts of US UNRATE, naive vs ARIMA(2,1,0).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nMODEL = \"ARIMA210_nc\"\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\nn_raw = len(raw)\ns = pd.Series(pd.to_numeric(raw[\"UNRATE\"], errors=\"coerce\").values,\n              index=pd.DatetimeIndex(pd.to_datetime(raw[\"observation_date\"]), freq=\"MS\"))\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nrows = []\nfor t in origins:\n    train = s.loc[:t]                       # only observations dated <= t\n    # naive: latest available observation dated <= t\n    naive = float(train.dropna().iloc[-1])\n    # model: ARIMA(2,1,0), no constant, Gaussian MLE via Kalman filter (handles NaN)\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, method=\"lbfgs\", maxiter=500)\n    fc = res.forecast(steps=max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        for name, f in ((\"naive\", naive), (MODEL, float(fc.iloc[h - 1]))):\n            rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(f, 10),\n                         \"actual\": (None if pd.isna(act) else float(act)), \"model\": name})\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n\ndef err(model, h):\n    d = fc_df[(fc_df.model == model) & (fc_df.horizon == h) & fc_df.actual.notna()].sort_values(\"origin_date\")\n    return (d.actual - d.forecast).to_numpy(dtype=float)\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 rectangular lags, HLN small-sample correction.\"\"\"\n    d = e1 ** 2 - e2 ** 2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        return None, None\n    dm = dbar / np.sqrt(v)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = dm * hln\n    p = 2 * stats.t.sf(abs(stat), df=n - 1)\n    return float(stat), float(p)\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n     \"model_description\": (\"ARIMA(2,1,0) without constant, i.e. AR(2) on first differences of UNRATE, \"\n                           \"estimated by Gaussian maximum likelihood (Kalman filter, statsmodels SARIMAX, missing months skipped) \"\n                           \"on an expanding window starting 1948-01-01 and ending at the origin, re-estimated at each origin; \"\n                           \"multi-step forecasts are iterated.\"),\n     \"n_origins\": int(len(origins)), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n     \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    assert len(en) == len(em)\n    R[f\"n_evaluated_h{h}\"] = int(len(en))\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    R[f\"rmse_naive_h{h}\"] = float(np.sqrt(np.mean(en ** 2)))\n    R[f\"mae_naive_h{h}\"] = float(np.mean(np.abs(en)))\nfor h in HORIZONS:\n    em = err(MODEL, h)\n    R[f\"rmse_model_h{h}\"] = float(np.sqrt(np.mean(em ** 2)))\n    R[f\"mae_model_h{h}\"] = float(np.mean(np.abs(em)))\nfor h in HORIZONS:\n    R[f\"rel_improvement_rmse_h{h}\"] = 1.0 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    st, p = dm_test(err(\"naive\", h), err(MODEL, h), h)\n    R[f\"dm_stat_h{h}\"] = st      # >0 means model has lower squared error\n    R[f\"dm_pvalue_h{h}\"] = p\nn_missing_targets = {h: int(((fc_df.model == \"naive\") & (fc_df.horizon == h) & fc_df.actual.isna()).sum()) for h in HORIZONS}\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [\n    {\"issue\": f\"Missing observation(s) in UNRATE.csv: {missing_dates}\",\n     \"handling\": (\"Naive at origin 2025-10-01 uses the 2025-09-01 value; the ARIMA likelihood/filter skips the missing month; \"\n                  f\"(origin,horizon) pairs targeting it are kept in forecasts.csv with empty actual and excluded from error statistics \"\n                  f\"(h1: {n_missing_targets[1]}, h3: {n_missing_targets[3]}).\")},\n    {\"issue\": \"Data are the final (revised) vintage retrieved 2026-10-30, not real-time vintages.\",\n     \"handling\": \"Evaluation is pseudo-out-of-sample on revised data; no real-time validity is claimed.\"},\n    {\"issue\": \"COVID-19 spike (2020-04 = 14.8%) lies inside every training window.\",\n     \"handling\": \"Retained (genuine observation); noted as a limitation for Gaussian MLE of the AR coefficients.\"},\n]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger: raw rows -> observations in the last training window\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with missing UNRATE value\", \"n_before\": n_raw, \"n_after\": n1,\n     \"n_removed\": n_raw - n1, \"rationale\": \"empty value field (2025-10-01); no data to use\"},\n    {\"step\": 2, \"rule\": \"drop observations dated after last origin (2026-06-01) from training\",\n     \"n_before\": n1, \"n_after\": R[\"n_analysed\"], \"n_removed\": n1 - R[\"n_analysed\"],\n     \"rationale\": \"information rule: training uses only obs <= origin; these serve only as evaluation actuals\"},\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_eval\", \"dm\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv && grep ',,' out/forecasts.csv","description":"Write and run analysis 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directory","sandboxed":false,"sourceToolAssistantUUID":"c583c1a1-a919-4079-80a4-8ddab10b0d8b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021/out/state","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"68fc721d-8710-4974-a63c-deb5d87b836d","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30","changed":true},"type":"attachment","uuid":"5bb83855-d377-4967-8506-fcf4501e85c9","timestamp":"2026-10-30T04:00:06.952Z","rendered":[{"content":"<system-reminder>\nThe date has changed. Today's date is now 2026-10-30. No need to announce the new date — the user's own clock shows it.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021/out/state","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"a77875897fdb5661503498976ac34ba440f5da3af10b93e328ea6b09250df18d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01VRAqAspkjcg8Lm9bmgfbS2","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin forecasts of US UNRATE, naive vs ARIMA(2,1,0).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nMODEL = \"ARIMA210_nc\"\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\nn_raw = len(raw)\ns = pd.Series(pd.to_numeric(raw[\"UNRATE\"], errors=\"coerce\").values,\n              index=pd.DatetimeIndex(pd.to_datetime(raw[\"observation_date\"]), freq=\"MS\"))\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nrows = []\nfor t in origins:\n    train = s.loc[:t]                       # only observations dated <= t\n    # naive: latest available observation dated <= t\n    naive = float(train.dropna().iloc[-1])\n    # model: ARIMA(2,1,0), no constant, Gaussian MLE via Kalman filter (handles NaN)\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, method=\"lbfgs\", maxiter=500)\n    fc = res.forecast(steps=max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        for name, f in ((\"naive\", naive), (MODEL, float(fc.iloc[h - 1]))):\n            rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(f, 10),\n                         \"actual\": (None if pd.isna(act) else float(act)), \"model\": name})\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n\ndef err(model, h):\n    d = fc_df[(fc_df.model == model) & (fc_df.horizon == h) & fc_df.actual.notna()].sort_values(\"origin_date\")\n    return (d.actual - d.forecast).to_numpy(dtype=float)\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 rectangular lags, HLN small-sample correction.\"\"\"\n    d = e1 ** 2 - e2 ** 2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        return None, None\n    dm = dbar / np.sqrt(v)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = dm * hln\n    p = 2 * stats.t.sf(abs(stat), df=n - 1)\n    return float(stat), float(p)\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n     \"model_description\": (\"ARIMA(2,1,0) without constant, i.e. AR(2) on first differences of UNRATE, \"\n                           \"estimated by Gaussian maximum likelihood (Kalman filter, statsmodels SARIMAX, missing months skipped) \"\n                           \"on an expanding window starting 1948-01-01 and ending at the origin, re-estimated at each origin; \"\n                           \"multi-step forecasts are iterated.\"),\n     \"n_origins\": int(len(origins)), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n     \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    assert len(en) == len(em)\n    R[f\"n_evaluated_h{h}\"] = int(len(en))\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    R[f\"rmse_naive_h{h}\"] = float(np.sqrt(np.mean(en ** 2)))\n    R[f\"mae_naive_h{h}\"] = float(np.mean(np.abs(en)))\nfor h in HORIZONS:\n    em = err(MODEL, h)\n    R[f\"rmse_model_h{h}\"] = float(np.sqrt(np.mean(em ** 2)))\n    R[f\"mae_model_h{h}\"] = float(np.mean(np.abs(em)))\nfor h in HORIZONS:\n    R[f\"rel_improvement_rmse_h{h}\"] = 1.0 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    st, p = dm_test(err(\"naive\", h), err(MODEL, h), h)\n    R[f\"dm_stat_h{h}\"] = st      # >0 means model has lower squared error\n    R[f\"dm_pvalue_h{h}\"] = p\nn_missing_targets = {h: int(((fc_df.model == \"naive\") & (fc_df.horizon == h) & fc_df.actual.isna()).sum()) for h in HORIZONS}\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [\n    {\"issue\": f\"Missing observation(s) in UNRATE.csv: {missing_dates}\",\n     \"handling\": (\"Naive at origin 2025-10-01 uses the 2025-09-01 value; the ARIMA likelihood/filter skips the missing month; \"\n                  f\"(origin,horizon) pairs targeting it are kept in forecasts.csv with empty actual and excluded from error statistics \"\n                  f\"(h1: {n_missing_targets[1]}, h3: {n_missing_targets[3]}).\")},\n    {\"issue\": \"Data are the final (revised) vintage retrieved 2026-10-30, not real-time vintages.\",\n     \"handling\": \"Evaluation is pseudo-out-of-sample on revised data; no real-time validity is claimed.\"},\n    {\"issue\": \"COVID-19 spike (2020-04 = 14.8%) lies inside every training window.\",\n     \"handling\": \"Retained (genuine observation); noted as a limitation for Gaussian MLE of the AR coefficients.\"},\n]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger: raw rows -> observations in the last training window\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with missing UNRATE value\", \"n_before\": n_raw, \"n_after\": n1,\n     \"n_removed\": n_raw - n1, \"rationale\": \"empty value field (2025-10-01); no data to use\"},\n    {\"step\": 2, \"rule\": \"drop observations dated after last origin (2026-06-01) from training\",\n     \"n_before\": n1, \"n_after\": R[\"n_analysed\"], \"n_removed\": n1 - R[\"n_analysed\"],\n     \"rationale\": \"information rule: training uses only obs <= origin; these serve only as evaluation actuals\"},\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_eval\", \"dm\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv && grep ',,' out/forecasts.csv","description":"Write and run analysis script"}}]}}40{"type":"api-request","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","id":"5e88c8f6-826e-4fc5-ba07-f904e6783dfb","timestamp":"2026-10-30T04:00:06.954Z","version":"2.1.314","querySource":"sdk","shapeHash":"b8d809ac82417bf397835e681565a3bdc00d93faf0e3078518fb99abc4c3b760","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"cb53bfb3-f144-43fb-9c6f-9b52399d8c5c","keep":10,"tail":["a77875897fdb5661503498976ac34ba440f5da3af10b93e328ea6b09250df18d","fd8c23bde9e18c1ac985dd4b5e061a50af1c74c8a7c40b1a98a763d093c47682"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7d228b3d02e2e19a12c8ee0900fbd6febe6018cfb7339e786ce1f668a0777f87"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5bb83855-d377-4967-8506-fcf4501e85c9","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}42{"type":"atis-latch","atis":"","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}43{"parentUuid":"5bb83855-d377-4967-8506-fcf4501e85c9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkegptB5dhnWbmV6iyE","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in `out/state`; 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writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nMODEL = \"ARIMA210_nc\"\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\nn_raw = len(raw)\ns = pd.Series(pd.to_numeric(raw[\"UNRATE\"], errors=\"coerce\").values,\n              index=pd.DatetimeIndex(pd.to_datetime(raw[\"observation_date\"]), freq=\"MS\"))\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nrows = []\nfor t in origins:\n    train = s.loc[:t]                       # only observations dated <= t\n    # naive: latest available observation dated <= t\n    naive = float(train.dropna().iloc[-1])\n    # model: ARIMA(2,1,0), no constant, Gaussian MLE via Kalman filter (handles NaN)\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, method=\"lbfgs\", maxiter=500)\n    fc = res.forecast(steps=max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        for name, f in ((\"naive\", naive), (MODEL, float(fc.iloc[h - 1]))):\n            rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(f, 10),\n                         \"actual\": (None if pd.isna(act) else float(act)), \"model\": name})\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n\ndef err(model, h):\n    d = fc_df[(fc_df.model == model) & (fc_df.horizon == h) & fc_df.actual.notna()].sort_values(\"origin_date\")\n    return (d.actual - d.forecast).to_numpy(dtype=float)\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 rectangular lags, HLN small-sample correction.\"\"\"\n    d = e1 ** 2 - e2 ** 2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        return None, None\n    dm = dbar / np.sqrt(v)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = dm * hln\n    p = 2 * stats.t.sf(abs(stat), df=n - 1)\n    return float(stat), float(p)\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n     \"model_description\": (\"ARIMA(2,1,0) without constant, i.e. AR(2) on first differences of UNRATE, \"\n                           \"estimated by Gaussian maximum likelihood (Kalman filter, statsmodels SARIMAX, missing months skipped) \"\n                           \"on an expanding window starting 1948-01-01 and ending at the origin, re-estimated at each origin; \"\n                           \"multi-step forecasts are iterated.\"),\n     \"n_origins\": int(len(origins)), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n     \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    assert len(en) == len(em)\n    R[f\"n_evaluated_h{h}\"] = int(len(en))\nfor h in HORIZONS:\n    en = err(\"naive\", h)\n    R[f\"rmse_naive_h{h}\"] = float(np.sqrt(np.mean(en ** 2)))\n    R[f\"mae_naive_h{h}\"] = float(np.mean(np.abs(en)))\nfor h in HORIZONS:\n    em = err(MODEL, h)\n    R[f\"rmse_model_h{h}\"] = float(np.sqrt(np.mean(em ** 2)))\n    R[f\"mae_model_h{h}\"] = float(np.mean(np.abs(em)))\nfor h in HORIZONS:\n    R[f\"rel_improvement_rmse_h{h}\"] = 1.0 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    st, p = dm_test(err(\"naive\", h), err(MODEL, h), h)\n    R[f\"dm_stat_h{h}\"] = st      # >0 means model has lower squared error\n    R[f\"dm_pvalue_h{h}\"] = p\nn_missing_targets = {h: int(((fc_df.model == \"naive\") & (fc_df.horizon == h) & fc_df.actual.isna()).sum()) for h in HORIZONS}\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [\n    {\"issue\": f\"Missing observation(s) in UNRATE.csv: {missing_dates}\",\n     \"handling\": (\"Naive at origin 2025-10-01 uses the 2025-09-01 value; the ARIMA likelihood/filter skips the missing month; \"\n                  f\"(origin,horizon) pairs targeting it are kept in forecasts.csv with empty actual and excluded from error statistics \"\n                  f\"(h1: {n_missing_targets[1]}, h3: {n_missing_targets[3]}).\")},\n    {\"issue\": \"Data are the final (revised) vintage retrieved 2026-10-30, not real-time vintages.\",\n     \"handling\": \"Evaluation is pseudo-out-of-sample on revised data; no real-time validity is claimed.\"},\n    {\"issue\": \"COVID-19 spike (2020-04 = 14.8%) lies inside every training window.\",\n     \"handling\": \"Retained (genuine observation); noted as a limitation for Gaussian MLE of the AR coefficients.\"},\n]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger: raw rows -> observations in the last training window\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with missing UNRATE value\", \"n_before\": n_raw, \"n_after\": n1,\n     \"n_removed\": n_raw - n1, \"rationale\": \"empty value field (2025-10-01); no data to use\"},\n    {\"step\": 2, \"rule\": \"drop observations dated after last origin (2026-06-01) from training\",\n     \"n_before\": n1, \"n_after\": R[\"n_analysed\"], \"n_removed\": n1 - R[\"n_analysed\"],\n     \"rationale\": \"information rule: training uses only obs <= origin; these serve only as evaluation actuals\"},\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_eval\", \"dm\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv && grep ',,' out/forecasts.csv; grep 2025-1 out/forecasts.csv","description":"Write and run analysis script from root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":196,"cache_read_input_tokens":15650,"output_tokens":3120,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":196},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXkeo7fFoStCF1aT5tkd","requestRef":"41a837ae-4986-4d13-b284-cc176f379ecd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"05808c93-97d9-4cc4-bcc4-5fa62764150e","timestamp":"2026-10-30T04:00:28.716Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"05808c93-97d9-4cc4-bcc4-5fa62764150e","isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FVcxPaDycrTaKX8qa5QuUv","type":"tool_result","content":"{\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.1197904141277979,\n \"mae_model_h1\": 0.09561928226000001,\n \"rmse_model_h3\": 0.15937419012351917,\n \"mae_model_h3\": 0.12090394601714284,\n \"rel_improvement_rmse_h1\": -0.022905396501212927,\n \"rel_improvement_rmse_h3\": -0.005103249283300526,\n \"dm_stat_h1\": -2.4559640134162657,\n \"dm_pvalue_h1\": 0.019315858251851482,\n \"dm_stat_h3\": -0.9643792812517766,\n \"dm_pvalue_h3\": 0.3416653293625982\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,empty value field (2025-10-01); no data to use\n2,drop observations dated after last origin (2026-06-01) from training,944,941,3,information rule: training uses only obs <= origin; these serve only as evaluation actuals\norigin_date,target_date,horizon,forecast,actual,model\n2023-07-01,2023-08-01,1,3.5,3.7,naive\n2023-07-01,2023-08-01,1,3.4961602811,3.7,ARIMA210_nc\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.5006303857,3.9,ARIMA210_nc\n     145 out/forecasts.csv\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3070637746,,ARIMA210_nc\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4037167521,,ARIMA210_nc\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3070637746,,ARIMA210_nc\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.2915682823,4.5,ARIMA210_nc\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4037167521,,ARIMA210_nc\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.3993282052,4.4,ARIMA210_nc\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.3996366342,4.5,ARIMA210_nc\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.3994888505,4.3,ARIMA210_nc\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.4994218311,4.4,ARIMA210_nc\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4974048598,4.4,ARIMA210_nc\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.3945466104,4.3,ARIMA210_nc\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.3989442657,4.3,ARIMA210_nc","is_error":false}]},"uuid":"c3d221a4-92e6-411d-99a6-1b83387a7ee7","timestamp":"2026-10-30T04:00:30.518Z","toolUseResult":{"stdout":"{\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.1197904141277979,\n \"mae_model_h1\": 0.09561928226000001,\n \"rmse_model_h3\": 0.15937419012351917,\n \"mae_model_h3\": 0.12090394601714284,\n \"rel_improvement_rmse_h1\": -0.022905396501212927,\n \"rel_improvement_rmse_h3\": -0.005103249283300526,\n \"dm_stat_h1\": -2.4559640134162657,\n \"dm_pvalue_h1\": 0.019315858251851482,\n \"dm_stat_h3\": -0.9643792812517766,\n \"dm_pvalue_h3\": 0.3416653293625982\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,empty value field (2025-10-01); no data to use\n2,drop observations dated after last origin (2026-06-01) from training,944,941,3,information rule: training uses only obs <= origin; these serve only as evaluation actuals\norigin_date,target_date,horizon,forecast,actual,model\n2023-07-01,2023-08-01,1,3.5,3.7,naive\n2023-07-01,2023-08-01,1,3.4961602811,3.7,ARIMA210_nc\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.5006303857,3.9,ARIMA210_nc\n     145 out/forecasts.csv\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3070637746,,ARIMA210_nc\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4037167521,,ARIMA210_nc\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3070637746,,ARIMA210_nc\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.2915682823,4.5,ARIMA210_nc\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4037167521,,ARIMA210_nc\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.3993282052,4.4,ARIMA210_nc\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.3996366342,4.5,ARIMA210_nc\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.3994888505,4.3,ARIMA210_nc\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.4994218311,4.4,ARIMA210_nc\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4974048598,4.4,ARIMA210_nc\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.3945466104,4.3,ARIMA210_nc\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.3989442657,4.3,ARIMA210_nc","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"05808c93-97d9-4cc4-bcc4-5fa62764150e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"c761f98bab7dccb95cc4ed560cb2499c51b026f4667dade92ee477960bb2ecca","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01FVcxPaDycrTaKX8qa5QuUv","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin forecasts of US UNRATE, naive vs ARIMA(2,1,0).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nMODEL = \"ARIMA210_nc\"\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\nn_raw = len(raw)\ns = pd.Series(pd.to_numeric(raw[\"UNRATE\"], errors=\"coerce\").values,\n              index=pd.DatetimeIndex(pd.to_datetime(raw[\"observation_date\"]), freq=\"MS\"))\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nrows = []\nfor t in origins:\n    train = s.loc[:t]                       # only observations dated <= t\n    # naive: latest available observation dated <= t\n    naive = float(train.dropna().iloc[-1])\n    # model: ARIMA(2,1,0), no constant, Gaussian MLE via Kalman filter (handles NaN)\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, method=\"lbfgs\", maxiter=500)\n    fc = res.forecast(steps=max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        for name, f in ((\"naive\", naive), (MODEL, float(fc.iloc[h - 1]))):\n            rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(f, 10),\n                         \"actual\": (None if pd.isna(act) else float(act)), \"model\": name})\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n\ndef err(model, h):\n    d = fc_df[(fc_df.model == model) & (fc_df.horizon == h) & fc_df.actual.notna()].sort_values(\"origin_date\")\n    return (d.actual - d.forecast).to_numpy(dtype=float)\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 rectangular lags, HLN small-sample correction.\"\"\"\n    d = e1 ** 2 - e2 ** 2\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        return None, None\n    dm = dbar / np.sqrt(v)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = dm * hln\n    p = 2 * stats.t.sf(abs(stat), df=n - 1)\n    return float(stat), float(p)\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n     \"model_description\": (\"ARIMA(2,1,0) without constant, i.e. AR(2) on first differences of UNRATE, \"\n                           \"estimated by Gaussian maximum likelihood (Kalman filter, statsmodels SARIMAX, missing months skipped) \"\n                           \"on an expanding window starting 1948-01-01 and ending at the origin, re-estimated at each origin; \"\n                           \"multi-step forecasts are iterated.\"),\n     \"n_origins\": int(len(origins)), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n     \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    en, em = err(\"naive\", h), err(MODEL, h)\n    assert len(en) == len(em)\n    R[f\"n_evaluated_h{h}\"] = int(len(en))\nfor h in HORIZONS:\n    en = err(\"naive\", h)\n    R[f\"rmse_naive_h{h}\"] = float(np.sqrt(np.mean(en ** 2)))\n    R[f\"mae_naive_h{h}\"] = float(np.mean(np.abs(en)))\nfor h in HORIZONS:\n    em = err(MODEL, h)\n    R[f\"rmse_model_h{h}\"] = float(np.sqrt(np.mean(em ** 2)))\n    R[f\"mae_model_h{h}\"] = float(np.mean(np.abs(em)))\nfor h in HORIZONS:\n    R[f\"rel_improvement_rmse_h{h}\"] = 1.0 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    st, p = dm_test(err(\"naive\", h), err(MODEL, h), h)\n    R[f\"dm_stat_h{h}\"] = st      # >0 means model has lower squared error\n    R[f\"dm_pvalue_h{h}\"] = p\nn_missing_targets = {h: int(((fc_df.model == \"naive\") & (fc_df.horizon == h) & fc_df.actual.isna()).sum()) for h in HORIZONS}\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [\n    {\"issue\": f\"Missing observation(s) in UNRATE.csv: {missing_dates}\",\n     \"handling\": (\"Naive at origin 2025-10-01 uses the 2025-09-01 value; the ARIMA likelihood/filter skips the missing month; \"\n                  f\"(origin,horizon) pairs targeting it are kept in forecasts.csv with empty actual and excluded from error statistics \"\n                  f\"(h1: {n_missing_targets[1]}, h3: {n_missing_targets[3]}).\")},\n    {\"issue\": \"Data are the final (revised) vintage retrieved 2026-10-30, not real-time vintages.\",\n     \"handling\": \"Evaluation is pseudo-out-of-sample on revised data; no real-time validity is claimed.\"},\n    {\"issue\": \"COVID-19 spike (2020-04 = 14.8%) lies inside every training window.\",\n     \"handling\": \"Retained (genuine observation); noted as a limitation for Gaussian MLE of the AR coefficients.\"},\n]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger: raw rows -> observations in the last training window\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    {\"step\": 1, \"rule\": \"drop rows with missing UNRATE value\", \"n_before\": n_raw, \"n_after\": n1,\n     \"n_removed\": n_raw - n1, \"rationale\": \"empty value field (2025-10-01); no data to use\"},\n    {\"step\": 2, \"rule\": \"drop observations dated after last origin (2026-06-01) from training\",\n     \"n_before\": n1, \"n_after\": R[\"n_analysed\"], \"n_removed\": n1 - R[\"n_analysed\"],\n     \"rationale\": \"information rule: training uses only obs <= origin; these serve only as evaluation actuals\"},\n])\nexcl.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_eval\", \"dm\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv && grep ',,' out/forecasts.csv; grep 2025-1 out/forecasts.csv","description":"Write and run analysis script from root"}}]}}51{"type":"api-request","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","id":"0479901d-1dfa-44d9-9758-2f48181b409a","timestamp":"2026-10-30T04:00:30.522Z","version":"2.1.314","querySource":"sdk","shapeHash":"b8d809ac82417bf397835e681565a3bdc00d93faf0e3078518fb99abc4c3b760","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"41a837ae-4986-4d13-b284-cc176f379ecd","keep":14,"tail":["c761f98bab7dccb95cc4ed560cb2499c51b026f4667dade92ee477960bb2ecca","23298b37ef3b5306530eaac3c4fca4e0b5f225dbfe1fd1a51d8ff26f8c379506"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a9c26c95162da0107eaebf8c5ace346cb76289fa1b3530d082b705a5ecf062ba"}52{"parentUuid":"c3d221a4-92e6-411d-99a6-1b83387a7ee7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkgRbQL1decVUbGs2QL","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSuAcKEAgTGAI4AUIIdGhpbmtpbmcSDBbgOop56zqT39lo0xoMziI0GqLWbqqaLFesIjBoR65ZQEFeKakT9T250PkbNE1Slmvgyg4p85TPzYLOU+veceEnt9wHO6yw+ZPsKP0q1QaI1w9YcAgcmAwZUZSlI0CsFeBdo5TxuTfDmyekUNE/IfIi9hOigBLpE9lJ7SaFaa133Z4a+ubnDstnsCpzA8YPHc+jBVZHk/I1j9OupBvziO5cg9+yDcs2a9rbktL7d/XCrSIp8iFJsk2U/96lVDnehRhIiCQxYbKEhWSQCT2L2GFWufegbLoImVzbAivuzjjue2M4IGYgdJM3HC9MawYJ/rLDre6kOuM3D8WA/DEWI8IElDMTKJl8XvudWViEPdb9OGbssQyXeWS4tWLNDy6zw8bAdk9YUu/r2ImAAtLvLX0KxMj+kxUnc9raYbg8yPd9h34LyZB2QznZf4DPKPfDpAhwTBY/5HhbytG2qa+9a/f1abk2J4ItVeUwrML9KobasJm8J0IZ+qWwJLTnWgtKr4+XXxptPWAaSI0AxaDSfM0yG85fUwulthg+++th4hDMquOzJzgRxiUzd/BcP/0SyTt44EKemsgyd4U4h1X+diG0mBOafsvAB4PGXliGNs9J96BLh2858mgATbrMWoTcAhP9EYaNVUHZgXjSrAKpt9SpUsM0gtTV1mLxX/pxuOvjw/lguUF5cIqxCJED3AI8S3utLakuODS49lLORAcmMtltnOn/GdlBsatzg+8q6xD7AlsiN8Tv3wOOrDbtr9GYrdsOwrb+KPkICgV2L1MnX4uqWXvJm2yWXOLrW2agMoz5mfaK5cdqj5W7BnWIpYtCoavFEOQz6JmN4xqtECJIzeqMYyOPHbRJY3qZcpMtR+xxKPUxKnvB5LZRBsTp05u/aW6ZQOtzokvqGlGCKjmP0671xqUNrRv7TB8byKdwEbGnsPgIOWB9LyYLQ9IghbI7sZNE1Nl0vMtme4Fn24UvjNwPRzoJ30coAD3lLO6Vkj2w3ohGXICIRVpVf76KQH9trhcKanFPRur6UUnyczNx9d/Q3ZIpBIbhvf9u5sfsRvjXaCj1FbT8fBuUkNnxsYJPgVCNbTymktQIGP6e/TedOmIAHra6gjEReDET7z0LEDUAVWjrakAymB6ncL/N1RyTyHKEvNNkGXdXs9+RDZHyxSGO+ER5PRrPbdGnKbZIGjHP0g5tBGz+M77VlnA1XK9LA5agnXiXCN5Kkxns2qr1o744tTtOGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4477,"cache_read_input_tokens":15846,"output_tokens":1066,"output_tokens_details":{"thinking_tokens":80},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4477},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":878,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkgRJYnWRGkuuq9bPZH","requestRef":"0479901d-1dfa-44d9-9758-2f48181b409a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"69f3c8be-f91d-4594-9d3e-11293cf236d1","timestamp":"2026-10-30T04:00:32.758Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"69f3c8be-f91d-4594-9d3e-11293cf236d1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkgRbQL1decVUbGs2QL","type":"message","role":"assistant","content":[{"type":"text","text":"Model runs. Now P4: reproducibility and consistency checks."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4477,"cache_read_input_tokens":15846,"output_tokens":1066,"output_tokens_details":{"thinking_tokens":80},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4477},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkgRJYnWRGkuuq9bPZH","requestRef":"0479901d-1dfa-44d9-9758-2f48181b409a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ddb5432d-0fbb-4cc2-b8b8-fcdfe3e1e8ff","timestamp":"2026-10-30T04:00:32.758Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"ddb5432d-0fbb-4cc2-b8b8-fcdfe3e1e8ff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkgRbQL1decVUbGs2QL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MZs54ioWEEo379xm1TmvS2","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nR=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv')\nc=[]\nc.append((\"144 ledger rows\", len(f)==144))\nc.append((\"36 unique origins\", f.origin_date.nunique()==36 and R['n_origins']==36))\nc.append((\"target = origin + h months\", all(pd.to_datetime(f.target_date)==[pd.Timestamp(o)+pd.DateOffset(months=int(h)) for o,h in zip(f.origin_date,f.horizon)])))\nc.append((\"n_eval + missing = 36 per h\", all(f[(f.model=='naive')&(f.horizon==h)].actual.notna().sum()==R[f'n_evaluated_h{h}']==35 for h in (1,3))))\nc.append((\"RMSE>=MAE>0\", all(R[f'rmse_{m}_h{h}']>=R[f'mae_{m}_h{h}']>0 for m in ('naive','model') for h in (1,3))))\nc.append((\"rel_impr identity\", all(abs(R[f'rel_improvement_rmse_h{h}']-(1-R[f'rmse_model_h{h}']/R[f'rmse_naive_h{h}']))<1e-12 for h in (1,3))))\nc.append((\"first<last origin\", R['first_origin']<R['last_origin']))\nc.append((\"exclusion ledger sums\", 945-1-3==R['n_analysed']==941))\nd=pd.read_csv('data/UNRATE.csv'); s=d.set_index('observation_date').UNRATE\nnv=f[f.model=='naive']\nc.append((\"naive = last obs <= origin\", all(v==s.loc[:o].dropna().iloc[-1] for o,v in zip(nv.origin_date,nv.forecast))))\nc.append((\"model forecasts within 0.2pp of naive (magnitude sanity)\", (abs(f[f.model!='naive'].forecast.values-nv.forecast.values)<0.2).all()))\nfor n,ok in c: print(ok,n)\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4477,"cache_read_input_tokens":15846,"output_tokens":1066,"output_tokens_details":{"thinking_tokens":80},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4477},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXkgRJYnWRGkuuq9bPZH","requestRef":"0479901d-1dfa-44d9-9758-2f48181b409a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a7fac1a1-22df-4fd8-9828-5c9722219608","timestamp":"2026-10-30T04:00:39.924Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}55{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a7fac1a1-22df-4fd8-9828-5c9722219608","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}56{"type":"atis-latch","atis":"","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}57{"parentUuid":"a7fac1a1-22df-4fd8-9828-5c9722219608","isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MZs54ioWEEo379xm1TmvS2","type":"tool_result","content":"IDENTICAL\nTrue 144 ledger rows\nTrue 36 unique origins\nTrue target = origin + h months\nTrue n_eval + missing = 36 per h\nTrue RMSE>=MAE>0\nTrue rel_impr identity\nTrue first<last origin\nTrue exclusion ledger sums\nTrue naive = last obs <= origin\nTrue model forecasts within 0.2pp of naive (magnitude sanity)","is_error":false}]},"uuid":"c918f48c-ee65-4a80-b10f-d1774176d09b","timestamp":"2026-10-30T04:00:41.974Z","toolUseResult":{"stdout":"IDENTICAL\nTrue 144 ledger rows\nTrue 36 unique origins\nTrue target = origin + h months\nTrue n_eval + missing = 36 per h\nTrue RMSE>=MAE>0\nTrue rel_impr identity\nTrue first<last origin\nTrue exclusion ledger sums\nTrue naive = last obs <= origin\nTrue model forecasts within 0.2pp of naive (magnitude sanity)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a7fac1a1-22df-4fd8-9828-5c9722219608","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"52aed4b88db12a181e87d80918b471a6b15c95a8a591c3a783b9ab8167e6df7d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Model runs. Now P4: reproducibility and consistency checks."},{"type":"tool_use","id":"toolu_01MZs54ioWEEo379xm1TmvS2","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nR=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv')\nc=[]\nc.append((\"144 ledger rows\", len(f)==144))\nc.append((\"36 unique origins\", f.origin_date.nunique()==36 and R['n_origins']==36))\nc.append((\"target = origin + h months\", all(pd.to_datetime(f.target_date)==[pd.Timestamp(o)+pd.DateOffset(months=int(h)) for o,h in zip(f.origin_date,f.horizon)])))\nc.append((\"n_eval + missing = 36 per h\", all(f[(f.model=='naive')&(f.horizon==h)].actual.notna().sum()==R[f'n_evaluated_h{h}']==35 for h in (1,3))))\nc.append((\"RMSE>=MAE>0\", all(R[f'rmse_{m}_h{h}']>=R[f'mae_{m}_h{h}']>0 for m in ('naive','model') for h in (1,3))))\nc.append((\"rel_impr identity\", all(abs(R[f'rel_improvement_rmse_h{h}']-(1-R[f'rmse_model_h{h}']/R[f'rmse_naive_h{h}']))<1e-12 for h in (1,3))))\nc.append((\"first<last origin\", R['first_origin']<R['last_origin']))\nc.append((\"exclusion ledger sums\", 945-1-3==R['n_analysed']==941))\nd=pd.read_csv('data/UNRATE.csv'); s=d.set_index('observation_date').UNRATE\nnv=f[f.model=='naive']\nc.append((\"naive = last obs <= origin\", all(v==s.loc[:o].dropna().iloc[-1] for o,v in zip(nv.origin_date,nv.forecast))))\nc.append((\"model forecasts within 0.2pp of naive (magnitude sanity)\", (abs(f[f.model!='naive'].forecast.values-nv.forecast.values)<0.2).all()))\nfor n,ok in c: print(ok,n)\nEOF","description":"Reproducibility and consistency checks"}}]}}59{"type":"api-request","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","id":"53612828-5fab-4e74-87d4-91621b82b495","timestamp":"2026-10-30T04:00:41.977Z","version":"2.1.314","querySource":"sdk","shapeHash":"b8d809ac82417bf397835e681565a3bdc00d93faf0e3078518fb99abc4c3b760","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"0479901d-1dfa-44d9-9758-2f48181b409a","keep":16,"tail":["52aed4b88db12a181e87d80918b471a6b15c95a8a591c3a783b9ab8167e6df7d","54e6576dc653dfc759c8415a4b39b4a05074d0dbec2e7c6438c94b8efb32ebc3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"92a4e7a4c146251da8cf8758e79bd6e6ffae4f0260ba0e47c85d934286a6c019"}60{"parentUuid":"c918f48c-ee65-4a80-b10f-d1774176d09b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhGfQdWek7us5v1KWF","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1199,"cache_read_input_tokens":20323,"output_tokens":658,"output_tokens_details":{"thinking_tokens":41},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1199},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":426,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkhGJLfLPpi97Xuf2No","requestRef":"53612828-5fab-4e74-87d4-91621b82b495","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"40a1601b-acd2-46f8-9d0d-8746196f85d4","timestamp":"2026-10-30T04:00:43.344Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"40a1601b-acd2-46f8-9d0d-8746196f85d4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhGfQdWek7us5v1KWF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DtdrEm2ntTq8sfK6ARdYY4","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results/forecasts/exclusions, re-ran analysis.py): byte-identical results.json and forecasts.csv | PASS |\n| forecasts.csv has 144 rows, 36 unique origins | PASS |\n| target_date = origin_date + h months for every row | PASS |\n| n_evaluated_h1 = n_evaluated_h3 = 35; 36 − 35 = 1 missing target per horizon (2025-10-01) | PASS |\n| RMSE ≥ MAE > 0 for all model/horizon pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive (to 1e-12) | PASS |\n| first_origin < last_origin | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| Naive forecast equals latest non-missing obs ≤ origin (incl. origin 2025-10-01 → 2025-09 value 4.4) | PASS |\n| Magnitude: model forecasts within 0.2 pp of naive; model RMSE within 3% of naive RMSE | PASS |\n| Information rule: training series truncated with s.loc[:t] at each origin (code inspection); model spec fixed a priori, not tuned on evaluation errors | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate (P4)"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1199,"cache_read_input_tokens":20323,"output_tokens":658,"output_tokens_details":{"thinking_tokens":41},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1199},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkhGJLfLPpi97Xuf2No","requestRef":"53612828-5fab-4e74-87d4-91621b82b495","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"38313c93-be3e-4e86-aa7e-2dcad155457d","timestamp":"2026-10-30T04:00:47.761Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"38313c93-be3e-4e86-aa7e-2dcad155457d","isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DtdrEm2ntTq8sfK6ARdYY4","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 41 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:n_evaluated_h1: ok\n  [ok] key:n_evaluated_h3: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2023-07-01 vs 2026-06-01\n  [ok] reproducibility: regenerated in 1.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RP0021/out/state/gate_receipts/20261030T040049.572960Z.json\n20261030T040049.572960Z.json","is_error":false}]},"uuid":"2fdedbe3-9f70-42cb-b8ae-09b3e00d8be4","timestamp":"2026-10-30T04:00:49.583Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 41 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:n_evaluated_h1: ok\n  [ok] key:n_evaluated_h3: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2023-07-01 vs 2026-06-01\n  [ok] reproducibility: regenerated in 1.7s; 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36 − 35 = 1 missing target per horizon (2025-10-01) | PASS |\n| RMSE ≥ MAE > 0 for all model/horizon pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive (to 1e-12) | PASS |\n| first_origin < last_origin | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| Naive forecast equals latest non-missing obs ≤ origin (incl. origin 2025-10-01 → 2025-09 value 4.4) | PASS |\n| Magnitude: model forecasts within 0.2 pp of naive; model RMSE within 3% of naive RMSE | PASS |\n| Information rule: training series truncated with s.loc[:t] at each origin (code inspection); model spec fixed a priori, not tuned on evaluation errors | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate (P4)"}}]}}64{"type":"api-request","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","id":"3be8d054-e6b1-437d-bc36-f59679cf6190","timestamp":"2026-10-30T04:00:49.587Z","version":"2.1.314","querySource":"sdk","shapeHash":"b8d809ac82417bf397835e681565a3bdc00d93faf0e3078518fb99abc4c3b760","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"53612828-5fab-4e74-87d4-91621b82b495","keep":18,"tail":["e884ea48fbedca99f5153844c71201af87a82cf060965e6381ff8055a4c70ebf","fec92408ecc81254b3c7049dd8cc201b6664acf62790bf6b2fceb7cc7ea55b8f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6da04dbde618d07ddcf96f24841f49fdc01186442d0a3aa2e07f123a3b6176af"}65{"parentUuid":"2fdedbe3-9f70-42cb-b8ae-09b3e00d8be4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhq5ogVyoC6SDbeH87","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1573,"cache_read_input_tokens":21522,"output_tokens":3674,"output_tokens_details":{"thinking_tokens":43},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":945,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkhppgVRyTXq3bf3fDn","requestRef":"3be8d054-e6b1-437d-bc36-f59679cf6190","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f330395c-fd57-4ef0-83d3-c0607722d38d","timestamp":"2026-10-30T04:00:51.266Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"f330395c-fd57-4ef0-83d3-c0607722d38d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhq5ogVyoC6SDbeH87","type":"message","role":"assistant","content":[{"type":"text","text":"The P4 gate run passed the contract and reproducibility checks. The only failures are P5 deliverables that don't exist yet. Next I'll write the report, the claims ledger and the phase ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1573,"cache_read_input_tokens":21522,"output_tokens":3674,"output_tokens_details":{"thinking_tokens":43},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkhppgVRyTXq3bf3fDn","requestRef":"3be8d054-e6b1-437d-bc36-f59679cf6190","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"94a38d77-2119-475d-ad31-e41a4b160c63","timestamp":"2026-10-30T04:00:51.266Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"94a38d77-2119-475d-ad31-e41a4b160c63","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhq5ogVyoC6SDbeH87","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018FtxBzqnR2XmoMWtoTyL1h","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver 36 expanding-window forecast origins (`first_origin` = 2023-07-01 to `last_origin` = 2026-06-01, `n_origins` = 36), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons of 1 and 3 months? We measure this with out-of-sample RMSE and MAE, and with the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- Input: `data/UNRATE.csv`, final vintage retrieved 2026-10-30. It has 945 data rows covering 1948-01 to 2026-09 (`n_input_rows`), and its SHA-256 matches the README. There are no duplicate or missing dates.\n- One missing value is listed in `data_issues`: 2025-10-01 has an empty field. Handling: at origin 2025-10-01 the naive forecast uses the 2025-09 value. The model's Kalman-filter likelihood skips the missing month. The two pairs whose target is 2025-10-01 (origin 2025-09 at h=1, origin 2025-07 at h=3) stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. This leaves 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 rows → drop 1 missing value → 944 → drop 3 observations dated after the last origin (used only as actuals) → 941 = `n_analysed`. This is the number of observations in the last training window.\n\n## Method\n- **Naive:** every horizon is forecast with the latest non-missing observation dated ≤ origin.\n- **Model (`model_name` = ARIMA210_nc):** see `model_description`. The model is AR(2) on first differences with no constant. It is estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter) on an expanding window from 1948-01 up to and including the origin, and re-estimated at each origin. Forecasts for 3 months are built by iterating the 1-month forecast. I fixed the specification before looking at any evaluation errors and did not try other models. There is no randomness, so `random_seed` is null.\n- **Information rule:** at each origin the series is cut at the origin date (`s.loc[:t]`) before any estimation or transformation.\n- **Evaluation:** errors are actual − forecast on the final-vintage data. A Diebold–Mariano test with squared-error loss, HAC variance using h−1 lags and the Harvey–Leybourne–Newbold small-sample correction is reported as a supplement.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE_model/RMSE_naive |\n|---|---|---|---|---|---|\n| h=1 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| h=3 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\nAll errors are in percentage points. The AR model does **not** beat the naive forecast at either horizon. Its RMSE is about 2.3% higher at h=1 and about 0.5% higher at h=3, and its MAE is also slightly higher at both horizons. The model's forecasts stay very close to the no-change forecast, always within 0.2 pp (see `out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** DM statistic = −2.46 (`dm_stat_h1`), p = 0.019 (`dm_pvalue_h1`). The model's squared-error loss is higher than the naive loss by a statistically detectable amount, but the difference in RMSE is small (`rel_improvement_rmse_h1`).\n- **h=3:** DM statistic = −0.96 (`dm_stat_h3`), p = 0.34 (`dm_pvalue_h3`). There is no detectable difference.\n- With only 35 evaluated pairs, overlapping errors at h=3, and a single 3-year window, these results are fragile. The DM test also does not account for the fact that the model nests the random walk. That issue matters most at h=1, where the DM p-value should be treated as approximate.\n\n## Limitations and open issues\n- Final-vintage data are used, so this is a pseudo-out-of-sample exercise. No real-time validity is claimed, because revisions to seasonal factors were not known at the origins.\n- Every training window contains the 2020 COVID spike (e.g. 14.8% in 2020-04). This may distort the Gaussian-MLE AR coefficients. I kept the full sample on purpose to avoid choosing the sample after seeing the evaluation results. A model fitted on a shorter or robust sample might do differently, but that was not evaluated.\n- Only one model was assessed, so the results say nothing about AR-type models in general or about other specifications.\n- The 2025-10 missing value lowers the evaluated count to 35 per horizon. The task is otherwise complete (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the ARIMA(2,1,0) model does not beat the naive forecast: its RMSE and MAE are both slightly higher (relative RMSE improvement about -2.3%).\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data, single 3-year window.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model does not beat the naive forecast: its RMSE and MAE are marginally higher (relative RMSE improvement about -0.5%).\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-month errors; small sample.\"},\n {\"id\":\"C3\",\"text\":\"The squared-loss difference is statistically detectable at h=1 (DM p about 0.02) but not at h=3 (p about 0.34).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"DM test ignores that the model nests the random walk; small sample; treat as approximate.\"},\n {\"id\":\"C4\",\"text\":\"Each horizon has 35 of 36 pairs evaluated because the 2025-10-01 observation is missing.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C5\",\"text\":\"The last training window contains 941 observations after 1 missing value and 3 post-origin months are excluded from the 945 input rows.\",\"results_keys\":[\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; out/state/contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, monthly grid complete, 0 duplicates\",\"1 missing value (2025-10-01) documented\",\"exclusions.csv sums: 945-1-3=941\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed with same evaluation rule: rmse_naive_h1/h3, mae_naive_h1/h3\",\"naive equals latest obs <= origin (verified)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"deterministic (no RNG); random_seed null\",\"training truncated at origin s.loc[:t]\"],\"notes\":\"ARIMA(2,1,0) no constant, fixed a priori\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"10 consistency checks PASS (checks.md)\",\"gate run 20261030T040049.572960Z: contract/reproducibility checks ok; failures only for P5 artifacts not yet written (report, claims, phase statuses)\"],\"notes\":\"\",\"gate_receipts\":[\"out/state/gate_receipts/20261030T040049.572960Z.json\"]},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, each figure cites a results key\",\"claims.json written, all keys exist in results.json\"],\"notes\":\"\",\"gate_receipts\":[]}}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nR=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')): assert all(k in R for k in c['results_keys']),c['id']\nprint('claims keys ok')\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29: P2/P3 done: analysis.py written; naive + ARIMA210_nc forecasts, 144 ledger rows.\n- 2026-10-29: P4 passed: clean re-run identical; checks.md all PASS; gate run (only P5 artifacts missing).\n- 2026-10-29: P5: report.md, claims.json written; final gate run.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, phases; run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1573,"cache_read_input_tokens":21522,"output_tokens":3674,"output_tokens_details":{"thinking_tokens":43},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXkhppgVRyTXq3bf3fDn","requestRef":"3be8d054-e6b1-437d-bc36-f59679cf6190","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ac18d4ce-3bb1-49a6-9fb3-3c0aec6df0c2","timestamp":"2026-10-30T04:01:19.044Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}68{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"ac18d4ce-3bb1-49a6-9fb3-3c0aec6df0c2","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}69{"type":"atis-latch","atis":"","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd"}70{"parentUuid":"ac18d4ce-3bb1-49a6-9fb3-3c0aec6df0c2","isSidechain":false,"promptId":"31d122bb-6adb-4503-9faa-124b851f7407","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018FtxBzqnR2XmoMWtoTyL1h","type":"tool_result","content":"claims keys ok\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RP0021/out/state/gate_receipts/20261030T040120.873744Z.json","is_error":false}]},"uuid":"1475d53d-bf3a-4e73-9b7e-d04c9b5122fc","timestamp":"2026-10-30T04:01:20.881Z","toolUseResult":{"stdout":"claims keys ok\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RP0021/out/state/gate_receipts/20261030T040120.873744Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ac18d4ce-3bb1-49a6-9fb3-3c0aec6df0c2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0021","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"6c89c7fd-c6e2-42ae-af54-1f1d109ce3fd","hash":"4635d6cd183d33bdae9691bc615a0b5141b384c4f98ac33f53c9efd981a06fa5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 gate run passed the contract and reproducibility checks. The only failures are P5 deliverables that don't exist yet. Next I'll write the report, the claims ledger and the phase ledger."},{"type":"tool_use","id":"toolu_018FtxBzqnR2XmoMWtoTyL1h","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver 36 expanding-window forecast origins (`first_origin` = 2023-07-01 to `last_origin` = 2026-06-01, `n_origins` = 36), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons of 1 and 3 months? We measure this with out-of-sample RMSE and MAE, and with the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- Input: `data/UNRATE.csv`, final vintage retrieved 2026-10-30. It has 945 data rows covering 1948-01 to 2026-09 (`n_input_rows`), and its SHA-256 matches the README. There are no duplicate or missing dates.\n- One missing value is listed in `data_issues`: 2025-10-01 has an empty field. Handling: at origin 2025-10-01 the naive forecast uses the 2025-09 value. The model's Kalman-filter likelihood skips the missing month. The two pairs whose target is 2025-10-01 (origin 2025-09 at h=1, origin 2025-07 at h=3) stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. This leaves 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 rows → drop 1 missing value → 944 → drop 3 observations dated after the last origin (used only as actuals) → 941 = `n_analysed`. This is the number of observations in the last training window.\n\n## Method\n- **Naive:** every horizon is forecast with the latest non-missing observation dated ≤ origin.\n- **Model (`model_name` = ARIMA210_nc):** see `model_description`. The model is AR(2) on first differences with no constant. It is estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter) on an expanding window from 1948-01 up to and including the origin, and re-estimated at each origin. Forecasts for 3 months are built by iterating the 1-month forecast. I fixed the specification before looking at any evaluation errors and did not try other models. There is no randomness, so `random_seed` is null.\n- **Information rule:** at each origin the series is cut at the origin date (`s.loc[:t]`) before any estimation or transformation.\n- **Evaluation:** errors are actual − forecast on the final-vintage data. A Diebold–Mariano test with squared-error loss, HAC variance using h−1 lags and the Harvey–Leybourne–Newbold small-sample correction is reported as a supplement.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE_model/RMSE_naive |\n|---|---|---|---|---|---|\n| h=1 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| h=3 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\nAll errors are in percentage points. The AR model does **not** beat the naive forecast at either horizon. Its RMSE is about 2.3% higher at h=1 and about 0.5% higher at h=3, and its MAE is also slightly higher at both horizons. The model's forecasts stay very close to the no-change forecast, always within 0.2 pp (see `out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** DM statistic = −2.46 (`dm_stat_h1`), p = 0.019 (`dm_pvalue_h1`). The model's squared-error loss is higher than the naive loss by a statistically detectable amount, but the difference in RMSE is small (`rel_improvement_rmse_h1`).\n- **h=3:** DM statistic = −0.96 (`dm_stat_h3`), p = 0.34 (`dm_pvalue_h3`). There is no detectable difference.\n- With only 35 evaluated pairs, overlapping errors at h=3, and a single 3-year window, these results are fragile. The DM test also does not account for the fact that the model nests the random walk. That issue matters most at h=1, where the DM p-value should be treated as approximate.\n\n## Limitations and open issues\n- Final-vintage data are used, so this is a pseudo-out-of-sample exercise. No real-time validity is claimed, because revisions to seasonal factors were not known at the origins.\n- Every training window contains the 2020 COVID spike (e.g. 14.8% in 2020-04). This may distort the Gaussian-MLE AR coefficients. I kept the full sample on purpose to avoid choosing the sample after seeing the evaluation results. A model fitted on a shorter or robust sample might do differently, but that was not evaluated.\n- Only one model was assessed, so the results say nothing about AR-type models in general or about other specifications.\n- The 2025-10 missing value lowers the evaluated count to 35 per horizon. The task is otherwise complete (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the ARIMA(2,1,0) model does not beat the naive forecast: its RMSE and MAE are both slightly higher (relative RMSE improvement about -2.3%).\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data, single 3-year window.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model does not beat the naive forecast: its RMSE and MAE are marginally higher (relative RMSE improvement about -0.5%).\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-month errors; small sample.\"},\n {\"id\":\"C3\",\"text\":\"The squared-loss difference is statistically detectable at h=1 (DM p about 0.02) but not at h=3 (p about 0.34).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"DM test ignores that the model nests the random walk; small sample; treat as approximate.\"},\n {\"id\":\"C4\",\"text\":\"Each horizon has 35 of 36 pairs evaluated because the 2025-10-01 observation is missing.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C5\",\"text\":\"The last training window contains 941 observations after 1 missing value and 3 post-origin months are excluded from the 945 input rows.\",\"results_keys\":[\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; out/state/contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, monthly grid complete, 0 duplicates\",\"1 missing value (2025-10-01) documented\",\"exclusions.csv sums: 945-1-3=941\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed with same evaluation rule: rmse_naive_h1/h3, mae_naive_h1/h3\",\"naive equals latest obs <= origin (verified)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"deterministic (no RNG); random_seed null\",\"training truncated at origin s.loc[:t]\"],\"notes\":\"ARIMA(2,1,0) no constant, fixed a priori\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"10 consistency checks PASS (checks.md)\",\"gate run 20261030T040049.572960Z: contract/reproducibility checks ok; failures only for P5 artifacts not yet written (report, claims, phase statuses)\"],\"notes\":\"\",\"gate_receipts\":[\"out/state/gate_receipts/20261030T040049.572960Z.json\"]},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, each figure cites a results key\",\"claims.json written, all keys exist in results.json\"],\"notes\":\"\",\"gate_receipts\":[]}}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nR=json.load(open('out/results.json'))\nfor c in json.load(open('out/claims.json')): assert all(k in R for k in c['results_keys']),c['id']\nprint('claims keys ok')\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29: P2/P3 done: analysis.py written; 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All deliverables are written and the final gate passed with 0 of 41 checks failing.\n\nI compared the naive forecast with one model, `ARIMA210_nc`: an autoregressive model with two lags, fitted to month-to-month changes with no constant. It was re-estimated at each of the 36 forecast origins (2023-07 to 2026-06) on all data from 1948 up to the origin. I fixed this specification before looking at any forecast errors and tried no others.\n\nErrors are in percentage points. \"Relative improvement\" is 1 − RMSE_model / RMSE_naive, so a negative value means the model is worse.\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | Relative improvement |\n|---|---|---|---|---|---|\n| 1 month | 0.1171 | 0.1198 | 0.0914 | 0.0956 | −2.3% |\n| 3 months | 0.1586 | 0.1594 | 0.1200 | 0.1209 | −0.5% |\n\n- **Significance:** I added a test of whether the two forecasts' squared errors really differ (Diebold–Mariano). At 1 month the model is detectably worse, though only slightly (p ≈ 0.02). At 3 months there is no detectable difference (p ≈ 0.34). The test is approximate here because the model contains the no-change forecast as a special case and the sample is small.\n- **Missing month:** the October 2025 value is empty in the data. The two forecasts that target it stay in `out/forecasts.csv` with a blank actual and are left out of the error statistics, so 35 of 36 pairs are scored at each horizon. The October 2025 origin's naive forecast uses the September value; the model skips the missing month when fitting.\n\n**Limitations:**\n- The data are today's revised series, so this is not a test of how the forecasts would have done in real time.\n- Every training window includes the 2020 COVID spike (14.8% in April 2020), which may distort the model's fitted coefficients. 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