feat(example): add working TimesFM forecast example with global temperature data

- Add NOAA GISTEMP global temperature anomaly dataset (36 months, 2022-2024) - Run TimesFM 1.0 PyTorch forecast for 2025 (12-month horizon) - Generate fan chart visualization with 80%/90% confidence intervals - Create comprehensive markdown report with findings and API notes API Discovery: - TimesFM 2.5 PyTorch checkpoint has file format issue (model.safetensors vs expected torch_model.ckpt) - Working API uses TimesFmHparams + TimesFmCheckpoint + TimesFm() constructor - Documented API in GitHub README differs from actual pip package Includes: - temperature_anomaly.csv (input data) - forecast_output.csv (point forecast + quantiles) - forecast_output.json (machine-readable output) - forecast_visualization.png (LFS-tracked) - run_forecast.py (reusable script) - visualize_forecast.py (chart generation) - run_example.sh (one-click runner) - README.md (full report with findings)
2026-03-27 07:09:27 +08:00 · 2026-02-21 15:25:52 -05:00
parent 98670bcf47
commit c7c5bc21ff
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--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/README.md
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/README.md
@@ -0,0 +1,178 @@
+# TimesFM Forecast Report: Global Temperature Anomaly (2025)
+
+**Model:** TimesFM 1.0 (200M) PyTorch  
+**Generated:** 2026-02-21  
+**Source:** NOAA GISTEMP Global Land-Ocean Temperature Index
+
+---
+
+## Executive Summary
+
+TimesFM forecasts a mean temperature anomaly of **1.19°C** for 2025, slightly below the 2024 average of 1.25°C. The model predicts continued elevated temperatures with a peak of 1.30°C in March 2025 and a minimum of 1.06°C in December 2025.
+
+---
+
+## Input Data
+
+### Historical Temperature Anomalies (2022-2024)
+
+| Date | Anomaly (°C) | Date | Anomaly (°C) | Date | Anomaly (°C) |
+|------|-------------|------|-------------|------|-------------|
+| 2022-01 | 0.89 | 2023-01 | 0.87 | 2024-01 | 1.22 |
+| 2022-02 | 0.89 | 2023-02 | 0.98 | 2024-02 | 1.35 |
+| 2022-03 | 1.02 | 2023-03 | 1.21 | 2024-03 | 1.34 |
+| 2022-04 | 0.88 | 2023-04 | 1.00 | 2024-04 | 1.26 |
+| 2022-05 | 0.85 | 2023-05 | 0.94 | 2024-05 | 1.15 |
+| 2022-06 | 0.88 | 2023-06 | 1.08 | 2024-06 | 1.20 |
+| 2022-07 | 0.88 | 2023-07 | 1.18 | 2024-07 | 1.24 |
+| 2022-08 | 0.90 | 2023-08 | 1.24 | 2024-08 | 1.30 |
+| 2022-09 | 0.88 | 2023-09 | 1.47 | 2024-09 | 1.28 |
+| 2022-10 | 0.95 | 2023-10 | 1.32 | 2024-10 | 1.27 |
+| 2022-11 | 0.77 | 2023-11 | 1.18 | 2024-11 | 1.22 |
+| 2022-12 | 0.78 | 2023-12 | 1.16 | 2024-12 | 1.20 |
+
+**Statistics:**
+- Total observations: 36 months
+- Mean anomaly: 1.09°C
+- Trend (2022→2024): +0.37°C
+
+---
+
+## Raw Forecast Output
+
+### Point Forecast and Confidence Intervals
+
+| Month | Point | 80% CI | 90% CI |
+|-------|-------|--------|--------|
+| 2025-01 | 1.259 | [1.141, 1.297] | [1.248, 1.324] |
+| 2025-02 | 1.286 | [1.141, 1.340] | [1.277, 1.375] |
+| 2025-03 | 1.295 | [1.127, 1.355] | [1.287, 1.404] |
+| 2025-04 | 1.221 | [1.035, 1.290] | [1.208, 1.331] |
+| 2025-05 | 1.170 | [0.969, 1.239] | [1.153, 1.289] |
+| 2025-06 | 1.146 | [0.942, 1.218] | [1.128, 1.270] |
+| 2025-07 | 1.170 | [0.950, 1.248] | [1.151, 1.300] |
+| 2025-08 | 1.203 | [0.971, 1.284] | [1.186, 1.341] |
+| 2025-09 | 1.191 | [0.959, 1.283] | [1.178, 1.335] |
+| 2025-10 | 1.149 | [0.908, 1.240] | [1.126, 1.287] |
+| 2025-11 | 1.080 | [0.836, 1.176] | [1.062, 1.228] |
+| 2025-12 | 1.061 | [0.802, 1.153] | [1.037, 1.217] |
+
+### JSON Output
+
+```json
+{
+  "model": "TimesFM 1.0 (200M) PyTorch",
+  "input": {
+    "source": "NOAA GISTEMP Global Temperature Anomaly",
+    "n_observations": 36,
+    "date_range": "2022-01 to 2024-12",
+    "mean_anomaly_c": 1.089
+  },
+  "forecast": {
+    "horizon": 12,
+    "dates": ["2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06",
+              "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12"],
+    "point": [1.259, 1.286, 1.295, 1.221, 1.170, 1.146, 1.170, 1.203, 1.191, 1.149, 1.080, 1.061]
+  },
+  "summary": {
+    "forecast_mean_c": 1.186,
+    "forecast_max_c": 1.295,
+    "forecast_min_c": 1.061,
+    "vs_last_year_mean": -0.067
+  }
+}
+```
+
+---
+
+## Visualization
+
+![Temperature Anomaly Forecast](forecast_visualization.png)
+
+---
+
+## Findings
+
+### Key Observations
+
+1. **Slight cooling trend expected**: The model forecasts a mean anomaly 0.07°C below 2024 levels, suggesting a potential stabilization after the record-breaking temperatures of 2023-2024.
+
+2. **Seasonal pattern preserved**: The forecast shows the expected seasonal variation with higher anomalies in late winter (Feb-Mar) and lower in late fall (Nov-Dec).
+
+3. **Widening uncertainty**: The 90% CI expands from ±0.04°C in January to ±0.08°C in December, reflecting typical forecast uncertainty growth over time.
+
+4. **Peak temperature**: March 2025 is predicted to have the highest anomaly at 1.30°C, potentially approaching the September 2023 record of 1.47°C.
+
+### Limitations
+
+- TimesFM is a zero-shot forecaster without physical climate model constraints
+- The 36-month training window may not capture multi-decadal climate trends
+- El Niño/La Niña cycles are not explicitly modeled
+
+### Recommendations
+
+- Use this forecast as a baseline comparison for physics-based climate models
+- Update forecast quarterly as new observations become available
+- Consider ensemble approaches combining TimesFM with other methods
+
+---
+
+## Reproducibility
+
+### Files
+
+| File | Description |
+|------|-------------|
+| `temperature_anomaly.csv` | Input data (36 months) |
+| `forecast_output.csv` | Point forecast with quantiles |
+| `forecast_output.json` | Machine-readable forecast |
+| `forecast_visualization.png` | Fan chart visualization |
+| `run_forecast.py` | Forecasting script |
+| `visualize_forecast.py` | Visualization script |
+| `run_example.sh` | One-click runner |
+
+### How to Reproduce
+
+```bash
+# Install dependencies
+uv pip install "timesfm[torch]" matplotlib pandas numpy
+
+# Run the complete example
+cd scientific-skills/timesfm-forecasting/examples/global-temperature
+./run_example.sh
+```
+
+---
+
+## Technical Notes
+
+### API Discovery
+
+The TimesFM PyTorch API differs from the GitHub README documentation:
+
+**Documented (GitHub README):**
+```python
+model = timesfm.TimesFm(
+    context_len=512,
+    horizon_len=128,
+    backend="gpu",
+)
+model.load_from_google_repo("google/timesfm-2.5-200m-pytorch")
+```
+
+**Actual Working API:**
+```python
+hparams = timesfm.TimesFmHparams(horizon_len=12)
+checkpoint = timesfm.TimesFmCheckpoint(
+    huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
+)
+model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
+```
+
+### TimesFM 2.5 PyTorch Issue
+
+The `google/timesfm-2.5-200m-pytorch` checkpoint downloads as `model.safetensors`, but the TimesFM loader expects `torch_model.ckpt`. This causes a `FileNotFoundError` at model load time. Using TimesFM 1.0 PyTorch resolves this issue.
+
+---
+
+*Report generated by TimesFM Forecasting Skill (claude-scientific-skills)*
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_output.csv
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_output.csv
@@ -0,0 +1,13 @@
+date,point_forecast,q10,q20,q30,q40,q50,q60,q70,q80,q90,q99
+2025-01-01,1.2593384,1.248188,1.140702,1.1880752,1.2137158,1.2394564,1.2593384,1.2767732,1.297132,1.32396,1.367888
+2025-02-01,1.2856668,1.2773758,1.1406044,1.1960833,1.2322671,1.2593892,1.2856668,1.3110137,1.3400218,1.3751202,1.4253658
+2025-03-01,1.2950127,1.2869918,1.126852,1.1876173,1.234988,1.2675052,1.2950127,1.328448,1.354729,1.4035482,1.4642649
+2025-04-01,1.2207624,1.2084007,1.0352504,1.1041918,1.151865,1.1853008,1.2207624,1.256663,1.2898555,1.3310349,1.4016538
+2025-05-01,1.1702554,1.153313,0.9691495,1.0431063,1.0932612,1.1276176,1.1702554,1.201966,1.2390311,1.2891905,1.3632389
+2025-06-01,1.1455553,1.1275499,0.94203794,1.0110554,1.0658777,1.1061188,1.1455553,1.1806211,1.2180579,1.2702757,1.345366
+2025-07-01,1.1702348,1.1510556,0.9503718,1.0347577,1.0847733,1.1287677,1.1702348,1.2114835,1.2482276,1.2997853,1.3807325
+2025-08-01,1.2026825,1.1859496,0.9709255,1.0594383,1.1106675,1.1579902,1.2026825,1.2399211,1.2842004,1.3408126,1.419526
+2025-09-01,1.1909748,1.1784849,0.95943713,1.0403702,1.103606,1.1511956,1.1909748,1.2390201,1.2832941,1.3354731,1.416972
+2025-10-01,1.1490841,1.1264795,0.9079477,0.99529266,1.0548235,1.1052223,1.1490841,1.1897774,1.240414,1.2868769,1.3775467
+2025-11-01,1.0804785,1.0624356,0.8361266,0.9259792,0.9882403,1.0386353,1.0804785,1.1281581,1.1759715,1.228377,1.3122478
+2025-12-01,1.0613453,1.0366092,0.80220693,0.89521873,0.9593707,1.0152239,1.0613453,1.1032857,1.15315,1.216908,1.2959521
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_output.json
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_output.json
@@ -0,0 +1,188 @@
+{
+  "model": "TimesFM 1.0 (200M) PyTorch",
+  "input": {
+    "source": "NOAA GISTEMP Global Temperature Anomaly",
+    "n_observations": 36,
+    "date_range": "2022-01 to 2024-12",
+    "mean_anomaly_c": 1.09
+  },
+  "forecast": {
+    "horizon": 12,
+    "dates": [
+      "2025-01",
+      "2025-02",
+      "2025-03",
+      "2025-04",
+      "2025-05",
+      "2025-06",
+      "2025-07",
+      "2025-08",
+      "2025-09",
+      "2025-10",
+      "2025-11",
+      "2025-12"
+    ],
+    "point": [
+      1.25933837890625,
+      1.285666823387146,
+      1.2950127124786377,
+      1.2207623720169067,
+      1.170255422592163,
+      1.1455552577972412,
+      1.1702347993850708,
+      1.2026824951171875,
+      1.1909748315811157,
+      1.1490840911865234,
+      1.080478549003601,
+      1.0613453388214111
+    ],
+    "quantiles": {
+      "10%": [
+        1.2481880187988281,
+        1.2773758172988892,
+        1.286991834640503,
+        1.2084007263183594,
+        1.1533130407333374,
+        1.1275498867034912,
+        1.1510555744171143,
+        1.1859495639801025,
+        1.1784849166870117,
+        1.1264795064926147,
+        1.0624356269836426,
+        1.036609172821045
+      ],
+      "20%": [
+        1.1407020092010498,
+        1.1406043767929077,
+        1.126852035522461,
+        1.0352504253387451,
+        0.9691494703292847,
+        0.9420379400253296,
+        0.9503718018531799,
+        0.970925509929657,
+        0.9594371318817139,
+        0.9079477190971375,
+        0.8361266255378723,
+        0.8022069334983826
+      ],
+      "30%": [
+        1.1880751848220825,
+        1.1960833072662354,
+        1.187617301940918,
+        1.104191780090332,
+        1.0431063175201416,
+        1.01105535030365,
+        1.0347577333450317,
+        1.0594383478164673,
+        1.040370225906372,
+        0.9952926635742188,
+        0.9259791970252991,
+        0.8952187299728394
+      ],
+      "40%": [
+        1.2137157917022705,
+        1.232267141342163,
+        1.2349879741668701,
+        1.151865005493164,
+        1.0932612419128418,
+        1.0658776760101318,
+        1.084773302078247,
+        1.1106674671173096,
+        1.1036059856414795,
+        1.0548235177993774,
+        0.9882403016090393,
+        0.9593706727027893
+      ],
+      "50%": [
+        1.2394564151763916,
+        1.2593891620635986,
+        1.267505168914795,
+        1.1853008270263672,
+        1.127617597579956,
+        1.1061187982559204,
+        1.128767728805542,
+        1.1579902172088623,
+        1.1511956453323364,
+        1.1052223443984985,
+        1.03863525390625,
+        1.0152238607406616
+      ],
+      "60%": [
+        1.25933837890625,
+        1.285666823387146,
+        1.2950127124786377,
+        1.2207623720169067,
+        1.170255422592163,
+        1.1455552577972412,
+        1.1702347993850708,
+        1.2026824951171875,
+        1.1909748315811157,
+        1.1490840911865234,
+        1.080478549003601,
+        1.0613453388214111
+      ],
+      "70%": [
+        1.27677321434021,
+        1.3110136985778809,
+        1.3284480571746826,
+        1.2566629648208618,
+        1.2019660472869873,
+        1.1806211471557617,
+        1.2114834785461426,
+        1.2399210929870605,
+        1.2390201091766357,
+        1.1897773742675781,
+        1.1281580924987793,
+        1.1032856702804565
+      ],
+      "80%": [
+        1.2971320152282715,
+        1.3400218486785889,
+        1.3547290563583374,
+        1.2898554801940918,
+        1.2390310764312744,
+        1.2180578708648682,
+        1.248227596282959,
+        1.2842004299163818,
+        1.2832940816879272,
+        1.240414023399353,
+        1.175971508026123,
+        1.153149962425232
+      ],
+      "90%": [
+        1.3239599466323853,
+        1.3751201629638672,
+        1.403548240661621,
+        1.3310348987579346,
+        1.2891905307769775,
+        1.2702757120132446,
+        1.2997852563858032,
+        1.3408125638961792,
+        1.3354730606079102,
+        1.286876916885376,
+        1.2283769845962524,
+        1.2169079780578613
+      ],
+      "99%": [
+        1.3678879737854004,
+        1.4253658056259155,
+        1.4642648696899414,
+        1.40165376663208,
+        1.3632389307022095,
+        1.3453660011291504,
+        1.380732536315918,
+        1.4195259809494019,
+        1.416972041130066,
+        1.3775466680526733,
+        1.3122477531433105,
+        1.2959520816802979
+      ]
+    }
+  },
+  "summary": {
+    "forecast_mean_c": 1.186,
+    "forecast_max_c": 1.295,
+    "forecast_min_c": 1.061,
+    "vs_last_year_mean": -0.067
+  }
+}
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_visualization.png
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/forecast_visualization.png
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/run_example.sh
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/run_example.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+# run_example.sh - Run the TimesFM temperature anomaly forecasting example
+#
+# This script:
+# 1. Runs the preflight system check
+# 2. Runs the TimesFM forecast
+# 3. Generates the visualization
+#
+# Usage:
+#   ./run_example.sh
+#
+# Prerequisites:
+#   - Python 3.10+
+#   - timesfm[torch] installed: uv pip install "timesfm[torch]"
+#   - matplotlib, pandas, numpy
+
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SKILL_ROOT="$(dirname "$(dirname "$SCRIPT_DIR")")"
+
+echo "============================================================"
+echo "  TimesFM Example: Global Temperature Anomaly Forecast"
+echo "============================================================"
+
+# Step 1: Preflight check
+echo ""
+echo "🔍 Step 1: Running preflight system check..."
+python3 "$SKILL_ROOT/scripts/check_system.py" || {
+    echo "❌ Preflight check failed. Please fix the issues above before continuing."
+    exit 1
+}
+
+# Step 2: Run forecast
+echo ""
+echo "📊 Step 2: Running TimesFM forecast..."
+cd "$SCRIPT_DIR"
+python3 run_forecast.py
+
+# Step 3: Generate visualization
+echo ""
+echo "📈 Step 3: Generating visualization..."
+python3 visualize_forecast.py
+
+echo ""
+echo "============================================================"
+echo "  ✅ Example complete!"
+echo "============================================================"
+echo ""
+echo "Output files:"
+echo "  - $SCRIPT_DIR/forecast_output.csv"
+echo "  - $SCRIPT_DIR/forecast_output.json"
+echo "  - $SCRIPT_DIR/forecast_visualization.png"
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/run_forecast.py
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/run_forecast.py
@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""
+Run TimesFM forecast on global temperature anomaly data.
+Generates forecast output CSV and JSON for the example.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+# Preflight check
+print("=" * 60)
+print("  TIMeSFM FORECAST - Global Temperature Anomaly Example")
+print("=" * 60)
+
+# Load data
+data_path = Path(__file__).parent / "temperature_anomaly.csv"
+df = pd.read_csv(data_path, parse_dates=["date"])
+df = df.sort_values("date").reset_index(drop=True)
+
+print(f"\n📊 Input Data: {len(df)} months of temperature anomalies")
+print(
+    f"   Date range: {df['date'].min().strftime('%Y-%m')} to {df['date'].max().strftime('%Y-%m')}"
+)
+print(f"   Mean anomaly: {df['anomaly_c'].mean():.2f}°C")
+print(
+    f"   Trend: {df['anomaly_c'].iloc[-12:].mean() - df['anomaly_c'].iloc[:12].mean():.2f}°C change (first to last year)"
+)
+
+# Prepare input for TimesFM
+# TimesFM expects a list of 1D numpy arrays
+input_series = df["anomaly_c"].values.astype(np.float32)
+
+# Load TimesFM 1.0 (PyTorch)
+# NOTE: TimesFM 2.5 PyTorch checkpoint has a file format issue at time of writing.
+# The model.safetensors file is not loadable via torch.load().
+# Using TimesFM 1.0 PyTorch which works correctly.
+print("\n🤖 Loading TimesFM 1.0 (200M) PyTorch...")
+import timesfm
+
+hparams = timesfm.TimesFmHparams(horizon_len=12)
+checkpoint = timesfm.TimesFmCheckpoint(
+    huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
+)
+model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
+
+# Forecast
+print("\n📈 Running forecast (12 months ahead)...")
+forecast_input = [input_series]
+frequency_input = [0]  # Monthly data
+
+point_forecast, experimental_quantile_forecast = model.forecast(
+    forecast_input,
+    freq=frequency_input,
+)
+
+print(f"   Point forecast shape: {point_forecast.shape}")
+print(f"   Quantile forecast shape: {experimental_quantile_forecast.shape}")
+
+# Extract results
+point = point_forecast[0]  # Shape: (horizon,)
+quantiles = experimental_quantile_forecast[0]  # Shape: (horizon, num_quantiles)
+
+# TimesFM quantiles: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99]
+# Index mapping: 0=10%, 1=20%, ..., 4=50% (median), ..., 9=99%
+quantile_labels = ["10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%", "99%"]
+
+# Create forecast dates (2025 monthly)
+last_date = df["date"].max()
+forecast_dates = pd.date_range(
+    start=last_date + pd.DateOffset(months=1), periods=12, freq="MS"
+)
+
+# Build output DataFrame
+output_df = pd.DataFrame(
+    {
+        "date": forecast_dates.strftime("%Y-%m-%d"),
+        "point_forecast": point,
+        "q10": quantiles[:, 0],
+        "q20": quantiles[:, 1],
+        "q30": quantiles[:, 2],
+        "q40": quantiles[:, 3],
+        "q50": quantiles[:, 4],  # Median
+        "q60": quantiles[:, 5],
+        "q70": quantiles[:, 6],
+        "q80": quantiles[:, 7],
+        "q90": quantiles[:, 8],
+        "q99": quantiles[:, 9],
+    }
+)
+
+# Save outputs
+output_dir = Path(__file__).parent
+output_df.to_csv(output_dir / "forecast_output.csv", index=False)
+
+# JSON output for the report
+output_json = {
+    "model": "TimesFM 1.0 (200M) PyTorch",
+    "input": {
+        "source": "NOAA GISTEMP Global Temperature Anomaly",
+        "n_observations": len(df),
+        "date_range": f"{df['date'].min().strftime('%Y-%m')} to {df['date'].max().strftime('%Y-%m')}",
+        "mean_anomaly_c": round(df["anomaly_c"].mean(), 3),
+    },
+    "forecast": {
+        "horizon": 12,
+        "dates": forecast_dates.strftime("%Y-%m").tolist(),
+        "point": point.tolist(),
+        "quantiles": {
+            label: quantiles[:, i].tolist() for i, label in enumerate(quantile_labels)
+        },
+    },
+    "summary": {
+        "forecast_mean_c": round(float(point.mean()), 3),
+        "forecast_max_c": round(float(point.max()), 3),
+        "forecast_min_c": round(float(point.min()), 3),
+        "vs_last_year_mean": round(
+            float(point.mean() - df["anomaly_c"].iloc[-12:].mean()), 3
+        ),
+    },
+}
+
+with open(output_dir / "forecast_output.json", "w") as f:
+    json.dump(output_json, f, indent=2)
+
+# Print summary
+print("\n" + "=" * 60)
+print("  FORECAST RESULTS")
+print("=" * 60)
+print(
+    f"\n📅 Forecast period: {forecast_dates[0].strftime('%Y-%m')} to {forecast_dates[-1].strftime('%Y-%m')}"
+)
+print(f"\n🌡️  Temperature Anomaly Forecast (°C above 1951-1980 baseline):")
+print(f"\n   {'Month':<10} {'Point':>8} {'80% CI':>15} {'90% CI':>15}")
+print(f"   {'-' * 10} {'-' * 8} {'-' * 15} {'-' * 15}")
+for i, (date, pt, q10, q90, q05, q95) in enumerate(
+    zip(
+        forecast_dates.strftime("%Y-%m"),
+        point,
+        quantiles[:, 1],  # 20%
+        quantiles[:, 7],  # 80%
+        quantiles[:, 0],  # 10%
+        quantiles[:, 8],  # 90%
+    )
+):
+    print(
+        f"   {date:<10} {pt:>8.3f} [{q10:>6.3f}, {q90:>6.3f}] [{q05:>6.3f}, {q95:>6.3f}]"
+    )
+
+print(f"\n📊 Summary Statistics:")
+print(f"   Mean forecast:  {point.mean():.3f}°C")
+print(
+    f"   Max forecast:   {point.max():.3f}°C (Month: {forecast_dates[point.argmax()].strftime('%Y-%m')})"
+)
+print(
+    f"   Min forecast:   {point.min():.3f}°C (Month: {forecast_dates[point.argmin()].strftime('%Y-%m')})"
+)
+print(f"   vs 2024 mean:   {point.mean() - df['anomaly_c'].iloc[-12:].mean():+.3f}°C")
+
+print(f"\n✅ Output saved to:")
+print(f"   {output_dir / 'forecast_output.csv'}")
+print(f"   {output_dir / 'forecast_output.json'}")
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/temperature_anomaly.csv
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/temperature_anomaly.csv
@@ -0,0 +1,37 @@
+date,anomaly_c
+2022-01-01,0.89
+2022-02-01,0.89
+2022-03-01,1.02
+2022-04-01,0.88
+2022-05-01,0.85
+2022-06-01,0.88
+2022-07-01,0.88
+2022-08-01,0.90
+2022-09-01,0.88
+2022-10-01,0.95
+2022-11-01,0.77
+2022-12-01,0.78
+2023-01-01,0.87
+2023-02-01,0.98
+2023-03-01,1.21
+2023-04-01,1.00
+2023-05-01,0.94
+2023-06-01,1.08
+2023-07-01,1.18
+2023-08-01,1.24
+2023-09-01,1.47
+2023-10-01,1.32
+2023-11-01,1.18
+2023-12-01,1.16
+2024-01-01,1.22
+2024-02-01,1.35
+2024-03-01,1.34
+2024-04-01,1.26
+2024-05-01,1.15
+2024-06-01,1.20
+2024-07-01,1.24
+2024-08-01,1.30
+2024-09-01,1.28
+2024-10-01,1.27
+2024-11-01,1.22
+2024-12-01,1.20
--- a/scientific-skills/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
+++ b/scientific-skills/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
@@ -0,0 +1,123 @@
+#!/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 Forecast: Global Temperature Anomaly (2025)\nAbove 1951-1980 Baseline",
+        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()