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)

Statistics:

• Total observations: 36 months
• Mean anomaly: 1.09°C
• Trend (2022→2024): +0.37°C

Raw Forecast Output

Point Forecast and Confidence Intervals

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

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

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 (scientific-agent-skills)