mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-03-27 07:09:27 +08:00
910bcfdc8b
- Change title from 'Above 1951-1980 Baseline' to clearer example description - New title: 'TimesFM Zero-Shot Forecast Example / 36-month Temperature Anomaly → 12-month Forecast' - Makes it clear this is a demonstration with limited input data
124 lines
3.2 KiB
Python
124 lines
3.2 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Visualize TimesFM forecast results for global temperature anomaly.
|
|
|
|
Generates a publication-quality figure showing:
|
|
- Historical data (2022-2024)
|
|
- Point forecast (2025)
|
|
- 80% and 90% confidence intervals (fan chart)
|
|
|
|
Usage:
|
|
python visualize_forecast.py
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
from pathlib import Path
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
# Configuration
|
|
EXAMPLE_DIR = Path(__file__).parent
|
|
INPUT_FILE = EXAMPLE_DIR / "temperature_anomaly.csv"
|
|
FORECAST_FILE = EXAMPLE_DIR / "forecast_output.json"
|
|
OUTPUT_FILE = EXAMPLE_DIR / "forecast_visualization.png"
|
|
|
|
|
|
def main() -> None:
|
|
# Load historical data
|
|
df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
|
|
|
|
# Load forecast results
|
|
with open(FORECAST_FILE) as f:
|
|
forecast = json.load(f)
|
|
|
|
# Extract forecast data
|
|
dates = pd.to_datetime(forecast["forecast"]["dates"])
|
|
point = np.array(forecast["forecast"]["point"])
|
|
q10 = np.array(forecast["forecast"]["quantiles"]["10%"])
|
|
q20 = np.array(forecast["forecast"]["quantiles"]["20%"])
|
|
q80 = np.array(forecast["forecast"]["quantiles"]["80%"])
|
|
q90 = np.array(forecast["forecast"]["quantiles"]["90%"])
|
|
|
|
# Create figure
|
|
fig, ax = plt.subplots(figsize=(12, 6))
|
|
|
|
# Plot historical data
|
|
ax.plot(
|
|
df["date"],
|
|
df["anomaly_c"],
|
|
color="#2563eb",
|
|
linewidth=1.5,
|
|
marker="o",
|
|
markersize=3,
|
|
label="Historical (NOAA GISTEMP)",
|
|
)
|
|
|
|
# Plot 90% CI (outer band)
|
|
ax.fill_between(dates, q10, q90, alpha=0.2, color="#dc2626", label="90% CI")
|
|
|
|
# Plot 80% CI (inner band)
|
|
ax.fill_between(dates, q20, q80, alpha=0.3, color="#dc2626", label="80% CI")
|
|
|
|
# Plot point forecast
|
|
ax.plot(
|
|
dates,
|
|
point,
|
|
color="#dc2626",
|
|
linewidth=2,
|
|
marker="s",
|
|
markersize=4,
|
|
label="TimesFM Forecast",
|
|
)
|
|
|
|
# Add vertical line at forecast boundary
|
|
ax.axvline(
|
|
x=df["date"].max(), color="#6b7280", linestyle="--", linewidth=1, alpha=0.7
|
|
)
|
|
|
|
# Formatting
|
|
ax.set_xlabel("Date", fontsize=12)
|
|
ax.set_ylabel("Temperature Anomaly (°C)", fontsize=12)
|
|
ax.set_title(
|
|
"TimesFM Zero-Shot Forecast Example\n36-month Temperature Anomaly → 12-month Forecast",
|
|
fontsize=14,
|
|
fontweight="bold",
|
|
)
|
|
|
|
# Add annotations
|
|
ax.annotate(
|
|
f"Mean forecast: {forecast['summary']['forecast_mean_c']:.2f}°C\n"
|
|
f"vs 2024: {forecast['summary']['vs_last_year_mean']:+.2f}°C",
|
|
xy=(dates[6], point[6]),
|
|
xytext=(dates[6], point[6] + 0.15),
|
|
fontsize=10,
|
|
arrowprops=dict(arrowstyle="->", color="#6b7280", lw=1),
|
|
bbox=dict(boxstyle="round,pad=0.3", facecolor="white", edgecolor="#6b7280"),
|
|
)
|
|
|
|
# Grid and legend
|
|
ax.grid(True, alpha=0.3)
|
|
ax.legend(loc="upper left", fontsize=10)
|
|
|
|
# Set y-axis limits
|
|
ax.set_ylim(0.7, 1.5)
|
|
|
|
# Rotate x-axis labels
|
|
plt.xticks(rotation=45, ha="right")
|
|
|
|
# Tight layout
|
|
plt.tight_layout()
|
|
|
|
# Save
|
|
fig.savefig(OUTPUT_FILE, dpi=150, bbox_inches="tight")
|
|
print(f"✅ Saved visualization to: {OUTPUT_FILE}")
|
|
|
|
plt.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|