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Zero-shot time series forecasting with Google's TimesFM foundation model.
Passed 19 of 22 test cases with the skill, and 9 of 22 without it.
and 16 more in the full report
12 fixed8 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to load the recommended 200M parameter PyTorch model checkpoint for standard zero-shot time series inference. What exact Hugging Face model repository identifier should be passed to `from_pretrained`?
| Checked | Without skill | With skill |
|---|---|---|
| Uses 'google/timesfm-2.5-200m-pytorch' as the repository string. | Missed | Passed |
The instructions your model reads when the skill loads.
------- | --------- | ---------- | ---- | ------- | | TimesFM 2.5 (recommended) | 200M | ≥ 4 GB | ≥ 2 GB | ~800 MB | up to 16,384 | | TimesFM 2.0 (archived) | 500M | ≥ 16 GB | ≥ 8 GB | ~2 GB | up to 2,048 | | TimesFM 1.0 (archived) | 200M | ≥ 8 GB | ≥ 4 GB | ~800 MB | up to 2,048 |
Recommendation: Always use TimesFM 2.5 unless you have a specific reason to use an older checkpoint. It is smaller, faster, and supports 8× longer context.
python scripts/check_system.py
# Using uv (recommended by this repo)
uv pip install timesfm[torch]
# Or using pip
pip install timesfm[torch]
# For JAX/Flax backend (faster on TPU/GPU)
uv pip install timesfm[flax]
# CUDA 12.1 (NVIDIA GPU)
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu121
# CPU only
pip install torch>=2.0.0 --index-url https://download.pytorch.org/whl/cpu
# Apple Silicon (MPS)
pip install torch>=2.0.0 # MPS support is built-in
test cases passed, vs 9 / 22 without
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