feat(animation): extend forecasts to final date with dynamic horizon

- Each forecast now extends to 2025-12 regardless of historical data length
- Step 1 (12 points): forecasts 36 months ahead to 2025-12
- Step 25 (36 points): forecasts 12 months ahead to 2025-12
- GIF shows full forecast horizon at every animation step

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

@@ -3,7 +3,7 @@
 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.
+and adding one point at a time. Each forecast extends to the final date (2025-12).
 
 Output: animation_data.json with all forecast steps
 """
@@ -19,7 +19,10 @@ import timesfm
 
 # Configuration
 MIN_CONTEXT = 12  # Minimum points to start forecasting
-HORIZON = 12  # Always forecast 12 months ahead
+MAX_HORIZON = (
+    36  # Max forecast length (when we have 12 points, forecast 36 months to 2025-12)
+)
+TOTAL_MONTHS = 48  # Total months from 2022-01 to 2025-12 (graph extent)
 INPUT_FILE = Path(__file__).parent / "temperature_anomaly.csv"
 OUTPUT_FILE = Path(__file__).parent / "animation_data.json"
 
@@ -27,6 +30,7 @@ OUTPUT_FILE = Path(__file__).parent / "animation_data.json"
 def main() -> None:
     print("=" * 60)
     print("  TIMESFM ANIMATION DATA GENERATOR")
+    print("  Dynamic horizon - forecasts always reach 2025-12")
     print("=" * 60)
 
     # Load data
@@ -42,9 +46,9 @@ def main() -> None:
     )
     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)
+    # Load TimesFM with max horizon (will truncate output for shorter forecasts)
+    print(f"\n🤖 Loading TimesFM 1.0 (200M) PyTorch (horizon={MAX_HORIZON})...")
+    hparams = timesfm.TimesFmHparams(horizon_len=MAX_HORIZON)
     checkpoint = timesfm.TimesFmCheckpoint(
         huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
     )
@@ -57,23 +61,32 @@ def main() -> None:
         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...")
+        # Calculate dynamic horizon: forecast enough to reach 2025-12
+        horizon = TOTAL_MONTHS - n_points
+
+        print(
+            f"\n📈 Step {step_num}/{total_steps}: Using {n_points} points, forecasting {horizon} months..."
+        )
 
         # Get historical data up to this point
         historical_values = all_values[:n_points]
         historical_dates = all_dates[:n_points]
 
-        # Run forecast
+        # Run forecast (model outputs MAX_HORIZON, we truncate to actual horizon)
         point, quantiles = model.forecast(
             [historical_values],
             freq=[0],
         )
 
+        # Truncate to actual horizon
+        point = point[0][:horizon]
+        quantiles = quantiles[0, :horizon, :]
+
         # Determine forecast dates
         last_date = historical_dates[-1]
         forecast_dates = pd.date_range(
             start=last_date + pd.DateOffset(months=1),
-            periods=HORIZON,
+            periods=horizon,
             freq="MS",
         )
 
@@ -81,22 +94,24 @@ def main() -> None:
         step_data = {
             "step": step_num,
             "n_points": n_points,
+            "horizon": horizon,
             "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(),
+            "point_forecast": point.tolist(),
+            "q10": quantiles[:, 0].tolist(),
+            "q20": quantiles[:, 1].tolist(),
+            "q80": quantiles[:, 7].tolist(),
+            "q90": quantiles[:, 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")
+        print(f"   Forecast to: {forecast_dates[-1].strftime('%Y-%m')}")
+        print(f"   Forecast mean: {point.mean():.3f}°C")
 
     # Create output
     output = {
@@ -104,7 +119,8 @@ def main() -> None:
             "model": "TimesFM 1.0 (200M) PyTorch",
             "total_steps": len(animation_steps),
             "min_context": MIN_CONTEXT,
-            "horizon": HORIZON,
+            "max_horizon": MAX_HORIZON,
+            "total_months": TOTAL_MONTHS,
             "data_source": "NOAA GISTEMP Global Temperature Anomaly",
             "full_date_range": f"{all_dates[0].strftime('%Y-%m')} to {all_dates[-1].strftime('%Y-%m')}",
         },
@@ -124,7 +140,7 @@ def main() -> None:
     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")
+    print(f"   Each forecast extends to 2025-12")
 
 
 if __name__ == "__main__":