# 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)*
