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diff --git a/scientific-skills/timesfm-forecasting/examples/global-temperature/generate_animation_data.py b/scientific-skills/timesfm-forecasting/examples/global-temperature/generate_animation_data.py new file mode 100644 index 0000000..3e703dc --- /dev/null +++ b/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() diff --git a/scientific-skills/timesfm-forecasting/examples/global-temperature/generate_gif.py b/scientific-skills/timesfm-forecasting/examples/global-temperature/generate_gif.py new file mode 100644 index 0000000..d1310e2 --- /dev/null +++ b/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() diff --git a/scientific-skills/timesfm-forecasting/examples/global-temperature/interactive_forecast.html b/scientific-skills/timesfm-forecasting/examples/global-temperature/interactive_forecast.html new file mode 100644 index 0000000..a94025f --- /dev/null +++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/interactive_forecast.html @@ -0,0 +1,569 @@ + + + + + + TimesFM Interactive Forecast Animation + + + + + +
+
+

TimesFM Zero-Shot Forecast Animation

+

Watch the forecast evolve as more data is added

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+ Historical Data Points + 12 / 36 +
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+ 2022-01 + 2022-12 → Forecast to 2023-12 +
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Forecast Mean
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0.86°C
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Forecast Max
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--
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Forecast Min
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--
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80% CI Width
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--
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+ Historical Data +
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+ Forecast +
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+ 80% Confidence Interval +
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+ 90% Confidence Interval +
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