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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
{
"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
Findings
Key Observations
-
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.
-
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).
-
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.
-
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
# 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):
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:
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)