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- 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
124 lines
3.2 KiB
Python
124 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Visualize TimesFM forecast results for global temperature anomaly.
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Generates a publication-quality figure showing:
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- Historical data (2022-2024)
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- Point forecast (2025)
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- 80% and 90% confidence intervals (fan chart)
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Usage:
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python visualize_forecast.py
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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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 / "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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# Load historical data
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df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
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# Load forecast results
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with open(FORECAST_FILE) as f:
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forecast = json.load(f)
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# Extract forecast data
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dates = pd.to_datetime(forecast["forecast"]["dates"])
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point = np.array(forecast["forecast"]["point"])
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q10 = np.array(forecast["forecast"]["quantiles"]["10%"])
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q20 = np.array(forecast["forecast"]["quantiles"]["20%"])
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q80 = np.array(forecast["forecast"]["quantiles"]["80%"])
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q90 = np.array(forecast["forecast"]["quantiles"]["90%"])
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# Create figure
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fig, ax = plt.subplots(figsize=(12, 6))
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# Plot historical data
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ax.plot(
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df["date"],
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df["anomaly_c"],
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color="#2563eb",
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linewidth=1.5,
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marker="o",
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markersize=3,
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label="Historical (NOAA GISTEMP)",
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)
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# Plot 90% CI (outer band)
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ax.fill_between(dates, q10, q90, alpha=0.2, color="#dc2626", label="90% CI")
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# Plot 80% CI (inner band)
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ax.fill_between(dates, q20, q80, alpha=0.3, color="#dc2626", label="80% CI")
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# Plot point forecast
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ax.plot(
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dates,
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point,
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color="#dc2626",
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linewidth=2,
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marker="s",
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markersize=4,
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label="TimesFM Forecast",
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)
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# Add vertical line at forecast boundary
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ax.axvline(
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x=df["date"].max(), color="#6b7280", linestyle="--", linewidth=1, alpha=0.7
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)
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# Formatting
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ax.set_xlabel("Date", fontsize=12)
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ax.set_ylabel("Temperature Anomaly (°C)", fontsize=12)
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ax.set_title(
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"TimesFM Zero-Shot Forecast Example\n36-month Temperature Anomaly → 12-month Forecast",
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fontsize=14,
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fontweight="bold",
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)
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# Add annotations
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ax.annotate(
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f"Mean forecast: {forecast['summary']['forecast_mean_c']:.2f}°C\n"
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f"vs 2024: {forecast['summary']['vs_last_year_mean']:+.2f}°C",
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xy=(dates[6], point[6]),
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xytext=(dates[6], point[6] + 0.15),
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fontsize=10,
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arrowprops=dict(arrowstyle="->", color="#6b7280", lw=1),
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bbox=dict(boxstyle="round,pad=0.3", facecolor="white", edgecolor="#6b7280"),
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)
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# Grid and legend
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ax.grid(True, alpha=0.3)
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ax.legend(loc="upper left", fontsize=10)
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# Set y-axis limits
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ax.set_ylim(0.7, 1.5)
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# Rotate x-axis labels
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plt.xticks(rotation=45, ha="right")
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# Tight layout
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plt.tight_layout()
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# Save
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fig.savefig(OUTPUT_FILE, dpi=150, bbox_inches="tight")
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print(f"✅ Saved visualization to: {OUTPUT_FILE}")
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plt.close()
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if __name__ == "__main__":
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main()
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