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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
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@@ -23,8 +23,8 @@ import pandas as pd
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# Configuration
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EXAMPLE_DIR = Path(__file__).parent
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INPUT_FILE = EXAMPLE_DIR / "temperature_anomaly.csv"
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FORECAST_FILE = EXAMPLE_DIR / "forecast_output.json"
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OUTPUT_FILE = EXAMPLE_DIR / "forecast_visualization.png"
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FORECAST_FILE = EXAMPLE_DIR / "output" / "forecast_output.json"
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OUTPUT_FILE = EXAMPLE_DIR / "output" / "forecast_visualization.png"
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def main() -> None:
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