Clayton Young
6c44413b7f
Add a self-contained AI agent skill for TimesFM that teaches coding agents (Claude Code, OpenCode, Cursor, Codex) how to use the TimesFM API correctly — safe model loading, zero-shot forecasting, covariate workflows, anomaly detection, and the most common pitfalls. Files added: - AGENTS.md — auto-loaded skill document (root of repo) - claude-skill/scripts/check_system.py — mandatory preflight RAM/GPU/disk checker - claude-skill/scripts/forecast_csv.py — CLI wrapper for CSV forecasting - claude-skill/references/ — ForecastConfig API ref, data prep, HW reqs - claude-skill/examples/global-temperature/ — basic forecast + PNG/GIF pipeline - claude-skill/examples/anomaly-detection/ — two-phase detrend+Z-score + quantile PI - claude-skill/examples/covariates-forecasting/ — forecast_with_covariates() XReg demo - .gitattributes — Git LFS rules for PNG/GIF binary outputs Contributed by Clayton Young / Superior Byte Works LLC (@borealBytes) Apache 2.0 — same license as this repository
59 lines
1.5 KiB
JSON
59 lines
1.5 KiB
JSON
{
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"description": "Synthetic retail sales data with covariates for TimesFM XReg demo",
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"note_on_real_data": "For real datasets (e.g., Kaggle Rossmann Store Sales), download to tempfile.mkdtemp() -- do NOT commit to this repo.",
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"stores": {
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"store_A": {
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"type": "premium",
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"region": "urban",
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"base_sales": 1000,
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"mean_sales_context": 1148.7
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},
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"store_B": {
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"type": "standard",
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"region": "suburban",
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"base_sales": 750,
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"mean_sales_context": 907.0
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},
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"store_C": {
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"type": "discount",
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"region": "rural",
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"base_sales": 500,
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"mean_sales_context": 645.3
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}
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},
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"dimensions": {
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"context_length": 24,
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"horizon_length": 12,
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"total_length": 36,
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"num_stores": 3,
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"csv_rows": 108
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},
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"covariates": {
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"dynamic_numerical": [
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"price"
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],
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"dynamic_categorical": [
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"promotion",
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"holiday",
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"day_of_week"
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],
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"static_categorical": [
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"store_type",
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"region"
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]
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},
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"effect_magnitudes": {
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"holiday": "+200 units per holiday week",
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"promotion": "+150 units per promotion week",
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"price": "-20 units per $1 above base price"
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},
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"xreg_modes": {
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"xreg + timesfm": "Regression on TimesFM residuals (default)",
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"timesfm + xreg": "TimesFM on regression residuals"
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},
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"bug_fixes_history": [
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"v1: Variable-shadowing -- all stores had identical covariates",
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"v2: Fixed shadowing; CONTEXT_LEN 48->24",
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"v3: Added component decomposition (base, price/promo/holiday effects); 2x2 sharex viz"
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]
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} |