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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)
This commit is contained in:
Clayton Young
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# Git LFS tracking for binary files
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# Images
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*.png filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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# Model weights and checkpoints
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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# Data files
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.feather filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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# Archives
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# TimesFM Forecast Report: Global Temperature Anomaly (2025)
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**Model:** TimesFM 1.0 (200M) PyTorch
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**Generated:** 2026-02-21
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**Source:** NOAA GISTEMP Global Land-Ocean Temperature Index
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---
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## Executive Summary
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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.
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---
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## Input Data
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### Historical Temperature Anomalies (2022-2024)
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| Date | Anomaly (°C) | Date | Anomaly (°C) | Date | Anomaly (°C) |
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|------|-------------|------|-------------|------|-------------|
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| 2022-01 | 0.89 | 2023-01 | 0.87 | 2024-01 | 1.22 |
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| 2022-02 | 0.89 | 2023-02 | 0.98 | 2024-02 | 1.35 |
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| 2022-03 | 1.02 | 2023-03 | 1.21 | 2024-03 | 1.34 |
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| 2022-04 | 0.88 | 2023-04 | 1.00 | 2024-04 | 1.26 |
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| 2022-05 | 0.85 | 2023-05 | 0.94 | 2024-05 | 1.15 |
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| 2022-06 | 0.88 | 2023-06 | 1.08 | 2024-06 | 1.20 |
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| 2022-07 | 0.88 | 2023-07 | 1.18 | 2024-07 | 1.24 |
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| 2022-08 | 0.90 | 2023-08 | 1.24 | 2024-08 | 1.30 |
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| 2022-09 | 0.88 | 2023-09 | 1.47 | 2024-09 | 1.28 |
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| 2022-10 | 0.95 | 2023-10 | 1.32 | 2024-10 | 1.27 |
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| 2022-11 | 0.77 | 2023-11 | 1.18 | 2024-11 | 1.22 |
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| 2022-12 | 0.78 | 2023-12 | 1.16 | 2024-12 | 1.20 |
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**Statistics:**
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- Total observations: 36 months
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- Mean anomaly: 1.09°C
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- Trend (2022→2024): +0.37°C
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---
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## Raw Forecast Output
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### Point Forecast and Confidence Intervals
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| Month | Point | 80% CI | 90% CI |
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|-------|-------|--------|--------|
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| 2025-01 | 1.259 | [1.141, 1.297] | [1.248, 1.324] |
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| 2025-02 | 1.286 | [1.141, 1.340] | [1.277, 1.375] |
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| 2025-03 | 1.295 | [1.127, 1.355] | [1.287, 1.404] |
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| 2025-04 | 1.221 | [1.035, 1.290] | [1.208, 1.331] |
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| 2025-05 | 1.170 | [0.969, 1.239] | [1.153, 1.289] |
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| 2025-06 | 1.146 | [0.942, 1.218] | [1.128, 1.270] |
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| 2025-07 | 1.170 | [0.950, 1.248] | [1.151, 1.300] |
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| 2025-08 | 1.203 | [0.971, 1.284] | [1.186, 1.341] |
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| 2025-09 | 1.191 | [0.959, 1.283] | [1.178, 1.335] |
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| 2025-10 | 1.149 | [0.908, 1.240] | [1.126, 1.287] |
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| 2025-11 | 1.080 | [0.836, 1.176] | [1.062, 1.228] |
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| 2025-12 | 1.061 | [0.802, 1.153] | [1.037, 1.217] |
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### JSON Output
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```json
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{
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"model": "TimesFM 1.0 (200M) PyTorch",
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"input": {
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"source": "NOAA GISTEMP Global Temperature Anomaly",
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"n_observations": 36,
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"date_range": "2022-01 to 2024-12",
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"mean_anomaly_c": 1.089
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},
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"forecast": {
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"horizon": 12,
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"dates": ["2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06",
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"2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12"],
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"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]
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},
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"summary": {
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"forecast_mean_c": 1.186,
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"forecast_max_c": 1.295,
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"forecast_min_c": 1.061,
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"vs_last_year_mean": -0.067
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}
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}
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```
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---
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## Visualization
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---
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## Findings
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### Key Observations
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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.
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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).
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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.
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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.
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### Limitations
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- TimesFM is a zero-shot forecaster without physical climate model constraints
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- The 36-month training window may not capture multi-decadal climate trends
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- El Niño/La Niña cycles are not explicitly modeled
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### Recommendations
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- Use this forecast as a baseline comparison for physics-based climate models
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- Update forecast quarterly as new observations become available
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- Consider ensemble approaches combining TimesFM with other methods
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---
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## Reproducibility
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### Files
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| File | Description |
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|------|-------------|
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| `temperature_anomaly.csv` | Input data (36 months) |
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| `forecast_output.csv` | Point forecast with quantiles |
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| `forecast_output.json` | Machine-readable forecast |
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| `forecast_visualization.png` | Fan chart visualization |
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| `run_forecast.py` | Forecasting script |
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| `visualize_forecast.py` | Visualization script |
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| `run_example.sh` | One-click runner |
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### How to Reproduce
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```bash
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# Install dependencies
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uv pip install "timesfm[torch]" matplotlib pandas numpy
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# Run the complete example
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cd scientific-skills/timesfm-forecasting/examples/global-temperature
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./run_example.sh
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```
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---
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## Technical Notes
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### API Discovery
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The TimesFM PyTorch API differs from the GitHub README documentation:
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**Documented (GitHub README):**
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```python
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model = timesfm.TimesFm(
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context_len=512,
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horizon_len=128,
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backend="gpu",
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)
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model.load_from_google_repo("google/timesfm-2.5-200m-pytorch")
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```
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**Actual Working API:**
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```python
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hparams = timesfm.TimesFmHparams(horizon_len=12)
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checkpoint = timesfm.TimesFmCheckpoint(
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huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
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)
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model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
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```
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### TimesFM 2.5 PyTorch Issue
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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.
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---
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*Report generated by TimesFM Forecasting Skill (claude-scientific-skills)*
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date,point_forecast,q10,q20,q30,q40,q50,q60,q70,q80,q90,q99
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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{
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"model": "TimesFM 1.0 (200M) PyTorch",
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"input": {
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"source": "NOAA GISTEMP Global Temperature Anomaly",
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"n_observations": 36,
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"date_range": "2022-01 to 2024-12",
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"mean_anomaly_c": 1.09
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},
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"forecast": {
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"horizon": 12,
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"dates": [
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"2025-01",
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"2025-02",
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"2025-03",
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"2025-04",
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"2025-05",
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"2025-06",
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"2025-07",
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"2025-08",
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"2025-09",
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"2025-10",
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"2025-11",
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"2025-12"
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],
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"point": [
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1.25933837890625,
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1.285666823387146,
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1.2950127124786377,
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1.2207623720169067,
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1.170255422592163,
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1.1455552577972412,
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1.1702347993850708,
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1.2026824951171875,
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1.1909748315811157,
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1.1490840911865234,
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1.080478549003601,
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1.0613453388214111
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],
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"quantiles": {
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"10%": [
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1.2481880187988281,
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1.2773758172988892,
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1.286991834640503,
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1.2084007263183594,
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1.1533130407333374,
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1.1275498867034912,
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1.1510555744171143,
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1.1859495639801025,
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1.1784849166870117,
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1.1264795064926147,
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1.0624356269836426,
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1.036609172821045
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],
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"20%": [
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1.1407020092010498,
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1.1406043767929077,
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1.126852035522461,
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1.0352504253387451,
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0.9691494703292847,
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0.9420379400253296,
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0.9503718018531799,
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0.970925509929657,
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0.9594371318817139,
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0.9079477190971375,
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0.8361266255378723,
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0.8022069334983826
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],
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"30%": [
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1.1880751848220825,
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1.1960833072662354,
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1.187617301940918,
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1.104191780090332,
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1.0431063175201416,
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1.01105535030365,
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1.0347577333450317,
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1.0594383478164673,
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1.040370225906372,
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0.9952926635742188,
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0.9259791970252991,
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0.8952187299728394
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],
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"40%": [
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1.2137157917022705,
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1.232267141342163,
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1.2349879741668701,
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1.151865005493164,
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|
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
|
||||||
|
}
|
||||||
|
}
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 152 KiB |
@@ -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"
|
||||||
@@ -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'}")
|
||||||
@@ -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
|
||||||
|
@@ -0,0 +1,123 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Visualize TimesFM forecast results for global temperature anomaly.
|
||||||
|
|
||||||
|
Generates a publication-quality figure showing:
|
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|
- Historical data (2022-2024)
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|
- Point forecast (2025)
|
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|
- 80% and 90% confidence intervals (fan chart)
|
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|
|
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|
Usage:
|
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|
python visualize_forecast.py
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|
"""
|
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|
|
||||||
|
from __future__ import annotations
|
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|
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
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|
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
# Configuration
|
||||||
|
EXAMPLE_DIR = Path(__file__).parent
|
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|
INPUT_FILE = EXAMPLE_DIR / "temperature_anomaly.csv"
|
||||||
|
FORECAST_FILE = EXAMPLE_DIR / "forecast_output.json"
|
||||||
|
OUTPUT_FILE = EXAMPLE_DIR / "forecast_visualization.png"
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
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|
# 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()
|
||||||
Reference in New Issue
Block a user