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_animation_data.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Generate animation data for interactive forecast visualization.
+
+This script runs TimesFM forecasts incrementally, starting with minimal data
+and adding one point at a time, saving all forecasts for an interactive slider.
+
+Output: animation_data.json with all forecast steps
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import timesfm
+
+# Configuration
+MIN_CONTEXT = 12  # Minimum points to start forecasting
+HORIZON = 12  # Always forecast 12 months ahead
+INPUT_FILE = Path(__file__).parent / "temperature_anomaly.csv"
+OUTPUT_FILE = Path(__file__).parent / "animation_data.json"
+
+
+def main() -> None:
+    print("=" * 60)
+    print("  TIMESFM ANIMATION DATA GENERATOR")
+    print("=" * 60)
+
+    # Load data
+    df = pd.read_csv(INPUT_FILE, parse_dates=["date"])
+    df = df.sort_values("date").reset_index(drop=True)
+
+    all_dates = df["date"].tolist()
+    all_values = df["anomaly_c"].values.astype(np.float32)
+
+    print(f"\n📊 Total data: {len(all_values)} months")
+    print(
+        f"   Date range: {all_dates[0].strftime('%Y-%m')} to {all_dates[-1].strftime('%Y-%m')}"
+    )
+    print(f"   Animation steps: {len(all_values) - MIN_CONTEXT + 1}")
+
+    # Load TimesFM
+    print("\n🤖 Loading TimesFM 1.0 (200M) PyTorch...")
+    hparams = timesfm.TimesFmHparams(horizon_len=HORIZON)
+    checkpoint = timesfm.TimesFmCheckpoint(
+        huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
+    )
+    model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
+
+    # Generate forecasts for each step
+    animation_steps = []
+
+    for n_points in range(MIN_CONTEXT, len(all_values) + 1):
+        step_num = n_points - MIN_CONTEXT + 1
+        total_steps = len(all_values) - MIN_CONTEXT + 1
+
+        print(f"\n📈 Step {step_num}/{total_steps}: Using {n_points} points...")
+
+        # Get historical data up to this point
+        historical_values = all_values[:n_points]
+        historical_dates = all_dates[:n_points]
+
+        # Run forecast
+        point, quantiles = model.forecast(
+            [historical_values],
+            freq=[0],
+        )
+
+        # Determine forecast dates
+        last_date = historical_dates[-1]
+        forecast_dates = pd.date_range(
+            start=last_date + pd.DateOffset(months=1),
+            periods=HORIZON,
+            freq="MS",
+        )
+
+        # Store step data
+        step_data = {
+            "step": step_num,
+            "n_points": n_points,
+            "last_historical_date": historical_dates[-1].strftime("%Y-%m"),
+            "historical_dates": [d.strftime("%Y-%m") for d in historical_dates],
+            "historical_values": historical_values.tolist(),
+            "forecast_dates": [d.strftime("%Y-%m") for d in forecast_dates],
+            "point_forecast": point[0].tolist(),
+            "q10": quantiles[0, :, 0].tolist(),
+            "q20": quantiles[0, :, 1].tolist(),
+            "q80": quantiles[0, :, 7].tolist(),
+            "q90": quantiles[0, :, 8].tolist(),
+        }
+
+        animation_steps.append(step_data)
+
+        # Show summary
+        print(f"   Last date: {historical_dates[-1].strftime('%Y-%m')}")
+        print(f"   Forecast mean: {point[0].mean():.3f}°C")
+
+    # Create output
+    output = {
+        "metadata": {
+            "model": "TimesFM 1.0 (200M) PyTorch",
+            "total_steps": len(animation_steps),
+            "min_context": MIN_CONTEXT,
+            "horizon": HORIZON,
+            "data_source": "NOAA GISTEMP Global Temperature Anomaly",
+            "full_date_range": f"{all_dates[0].strftime('%Y-%m')} to {all_dates[-1].strftime('%Y-%m')}",
+        },
+        "actual_data": {
+            "dates": [d.strftime("%Y-%m") for d in all_dates],
+            "values": all_values.tolist(),
+        },
+        "animation_steps": animation_steps,
+    }
+
+    # Save
+    with open(OUTPUT_FILE, "w") as f:
+        json.dump(output, f, indent=2)
+
+    print(f"\n" + "=" * 60)
+    print("  ✅ ANIMATION DATA COMPLETE")
+    print("=" * 60)
+    print(f"\n📁 Output: {OUTPUT_FILE}")
+    print(f"   Total steps: {len(animation_steps)}")
+    print(f"   Each step shows forecast as one more data point is added")
+
+
+if __name__ == "__main__":
+    main()