Files
claude-scientific-skills/scientific-skills/timesfm-forecasting/examples/covariates-forecasting/output/covariates_metadata.json
Clayton Young df58339850 feat(timesfm): complete all three examples with quality docs
- anomaly-detection: full two-phase rewrite (context Z-score + forecast PI),
  2-panel viz, Sep 2023 correctly flagged CRITICAL (z=+3.03)
- covariates-forecasting: v3 rewrite with variable-shadowing bug fixed,
  2x2 shared-axis viz showing actionable covariate decomposition,
  108-row CSV with distinct per-store price arrays
- global-temperature: output/ subfolder reorganization (all 6 output files
  moved, 5 scripts + shell script paths updated)
- SKILL.md: added Examples table, Quality Checklist, Common Mistakes (8 items),
  Validation & Verification with regression assertions
- .gitattributes already at repo root covering all binary types
2026-02-23 07:43:04 -05:00

59 lines
1.5 KiB
JSON

{
"description": "Synthetic retail sales data with covariates for TimesFM XReg demo",
"note_on_real_data": "For real datasets (e.g., Kaggle Rossmann Store Sales), download to tempfile.mkdtemp() -- do NOT commit to this repo.",
"stores": {
"store_A": {
"type": "premium",
"region": "urban",
"base_sales": 1000,
"mean_sales_context": 1148.7
},
"store_B": {
"type": "standard",
"region": "suburban",
"base_sales": 750,
"mean_sales_context": 907.0
},
"store_C": {
"type": "discount",
"region": "rural",
"base_sales": 500,
"mean_sales_context": 645.3
}
},
"dimensions": {
"context_length": 24,
"horizon_length": 12,
"total_length": 36,
"num_stores": 3,
"csv_rows": 108
},
"covariates": {
"dynamic_numerical": [
"price"
],
"dynamic_categorical": [
"promotion",
"holiday",
"day_of_week"
],
"static_categorical": [
"store_type",
"region"
]
},
"effect_magnitudes": {
"holiday": "+200 units per holiday week",
"promotion": "+150 units per promotion week",
"price": "-20 units per $1 above base price"
},
"xreg_modes": {
"xreg + timesfm": "Regression on TimesFM residuals (default)",
"timesfm + xreg": "TimesFM on regression residuals"
},
"bug_fixes_history": [
"v1: Variable-shadowing -- all stores had identical covariates",
"v2: Fixed shadowing; CONTEXT_LEN 48->24",
"v3: Added component decomposition (base, price/promo/holiday effects); 2x2 sharex viz"
]
}