4 Commits

Author	SHA1	Message	Date
Timothy Kassis	f8da4bf9a7	Forecasting examples	2026-02-23 10:50:24 -08:00
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
Clayton Young	509190118f	fix(examples): correct quantile indices, variable shadowing, and test design in anomaly + covariates examples ... Anomaly detection fixes: - Fix critical quantile index bug: index 0 is mean not q10; correct indices are q10=1, q20=2, q80=8, q90=9 - Redesign test: use all 36 months as context, inject 3 synthetic anomalies into future - Result: 3 CRITICAL detected (was 11/12 — caused by test-set leakage + wrong indices) - Update severity labels: CRITICAL = outside 80% PI, WARNING = outside 60% PI Covariates fixes: - Fix variable-shadowing bug: inner dict comprehension overwrote outer loop store_id causing all stores to get identical covariate arrays (store_A's price for everyone) - Give each store a distinct price baseline (premium $12, standard $10, discount $7.50) - Trim CONTEXT_LEN from 48 → 24 weeks; CSV now 108 rows (was 180) - Add NOTE ON REAL DATA comment: temp file pattern for large external datasets Both scripts regenerated with clean outputs.	2026-02-23 07:43:04 -05:00
Clayton Young	0d98fa353c	feat(examples): add anomaly detection and covariates examples ... Anomaly Detection Example: - Uses quantile forecasts as prediction intervals - Flags values outside 80%/90% CI as warnings/critical anomalies - Includes visualization with deviation plot Covariates (XReg) Example: - Demonstrates forecast_with_covariates() API - Shows dynamic numerical/categorical covariates - Shows static categorical covariates - Includes synthetic retail sales data with price, promotion, holiday SKILL.md Updates: - Added anomaly detection section with code example - Expanded covariates section with covariate types table - Added XReg modes explanation - Updated 'When not to use' section to note anomaly detection workaround	2026-02-23 07:43:04 -05:00