feat(example): add interactive forecast animation with slider

Create an all-out demonstration showing how TimesFM forecasts evolve
as more historical data is added:

- generate_animation_data.py: Runs 25 incremental forecasts (12→36 points)
- interactive_forecast.html: Single-file HTML with Chart.js slider
  - Play/Pause animation control
  - Shows historical data, forecast, 80%/90% CIs, and actual future data
  - Live stats: forecast mean, max, min, CI width
- generate_gif.py: Creates animated GIF for embedding in markdown
- forecast_animation.gif: 25-frame animation (896 KB)

Interactive features:
- Slider to manually step through forecast evolution
- Auto-play with 500ms per frame
- Shows how each additional data point changes the forecast
- Confidence intervals narrow as more data is added

scientific-skills/timesfm-forecasting/examples/global-temperature/generate_gif.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""
+Generate animated GIF showing forecast evolution.
+
+Creates a GIF animation showing how the TimesFM forecast changes
+as more historical data points are added.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+# Configuration
+EXAMPLE_DIR = Path(__file__).parent
+DATA_FILE = EXAMPLE_DIR / "animation_data.json"
+OUTPUT_FILE = EXAMPLE_DIR / "forecast_animation.gif"
+DURATION_MS = 500  # Time per frame in milliseconds
+
+
+def create_frame(ax, step_data: dict, actual_data: dict, total_steps: int) -> None:
+    """Create a single frame of the animation."""
+    ax.clear()
+
+    # Parse dates
+    historical_dates = pd.to_datetime(step_data["historical_dates"])
+    forecast_dates = pd.to_datetime(step_data["forecast_dates"])
+
+    # Plot historical data
+    ax.plot(
+        historical_dates,
+        step_data["historical_values"],
+        color="#3b82f6",
+        linewidth=2,
+        marker="o",
+        markersize=4,
+        label="Historical",
+    )
+
+    # Plot 90% CI (outer)
+    ax.fill_between(
+        forecast_dates,
+        step_data["q10"],
+        step_data["q90"],
+        alpha=0.1,
+        color="#ef4444",
+        label="90% CI",
+    )
+
+    # Plot 80% CI (inner)
+    ax.fill_between(
+        forecast_dates,
+        step_data["q20"],
+        step_data["q80"],
+        alpha=0.2,
+        color="#ef4444",
+        label="80% CI",
+    )
+
+    # Plot forecast
+    ax.plot(
+        forecast_dates,
+        step_data["point_forecast"],
+        color="#ef4444",
+        linewidth=2,
+        marker="s",
+        markersize=4,
+        label="Forecast",
+    )
+
+    # Plot actual future data if available
+    actual_dates = pd.to_datetime(actual_data["dates"])
+    actual_values = actual_data["values"]
+
+    # Find which actual points fall in forecast period
+    forecast_start = forecast_dates[0]
+    forecast_end = forecast_dates[-1]
+
+    future_mask = (actual_dates >= forecast_start) & (actual_dates <= forecast_end)
+    future_dates = actual_dates[future_mask]
+    future_values = np.array(actual_values)[future_mask]
+
+    if len(future_dates) > 0:
+        ax.plot(
+            future_dates,
+            future_values,
+            color="#10b981",
+            linewidth=1,
+            linestyle="--",
+            marker="o",
+            markersize=3,
+            alpha=0.7,
+            label="Actual (future)",
+        )
+
+    # Add vertical line at forecast boundary
+    ax.axvline(
+        x=historical_dates[-1],
+        color="#6b7280",
+        linestyle="--",
+        linewidth=1,
+        alpha=0.5,
+    )
+
+    # Formatting
+    ax.set_xlabel("Date", fontsize=11)
+    ax.set_ylabel("Temperature Anomaly (°C)", fontsize=11)
+    ax.set_title(
+        f"TimesFM Forecast Evolution\n"
+        f"Step {step_data['step']}/{total_steps}: {step_data['n_points']} points → {step_data['last_historical_date']}",
+        fontsize=13,
+        fontweight="bold",
+    )
+
+    ax.grid(True, alpha=0.3)
+    ax.legend(loc="upper left", fontsize=9)
+    ax.set_ylim(0.5, 1.6)
+
+    # Format x-axis
+    ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
+    ax.xaxis.set_major_locator(mdates.MonthLocator(interval=6))
+    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, ha="right")
+
+
+def main() -> None:
+    print("=" * 60)
+    print("  GENERATING ANIMATED GIF")
+    print("=" * 60)
+
+    # Load data
+    with open(DATA_FILE) as f:
+        data = json.load(f)
+
+    total_steps = len(data["animation_steps"])
+    print(f"\n📊 Total frames: {total_steps}")
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # Generate frames
+    frames = []
+
+    for i, step in enumerate(data["animation_steps"]):
+        print(f"   Frame {i + 1}/{total_steps}...")
+
+        create_frame(ax, step, data["actual_data"], total_steps)
+
+        # Save frame to buffer
+        fig.canvas.draw()
+
+        # Convert to PIL Image
+        buf = fig.canvas.buffer_rgba()
+        width, height = fig.canvas.get_width_height()
+        img = Image.frombytes("RGBA", (width, height), buf)
+        frames.append(img.convert("RGB"))
+
+    plt.close()
+
+    # Save as GIF
+    print(f"\n💾 Saving GIF: {OUTPUT_FILE}")
+    frames[0].save(
+        OUTPUT_FILE,
+        save_all=True,
+        append_images=frames[1:],
+        duration=DURATION_MS,
+        loop=0,  # Loop forever
+    )
+
+    # Get file size
+    size_kb = OUTPUT_FILE.stat().st_size / 1024
+    print(f"   File size: {size_kb:.1f} KB")
+    print(f"\n✅ Done!")
+
+
+if __name__ == "__main__":
+    main()