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Cataloged from google/timesfm-2.5-200m-transformers
All models by google →TimesFM (Time Series Foundation Model) is a pretrained decoder-only model for time-series forecasting. This repository contains the Transformers port of the official TimesFM 2.5 PyTorch release.
Resources and Technical Documentation:
This model is converted from the official TimesFM 2.5 PyTorch checkpoint and integrated into transformers as TimesFm2_5ModelForPrediction.
The converted checkpoint preserves the original architecture and forecasting behavior, including:
This checkpoint was produced with:
src/transformers/models/timesfm_2p5/convert_timesfm_2p5_original_to_hf.pygoogle/timesfm-2.5-200m-pytorch2026-02-20Weight conversion parity is verified by comparing converted-model forecasts against the official implementation outputs on deterministic inputs.
import torchfrom transformers import TimesFm2_5ModelForPrediction
model = TimesFm2_5ModelForPrediction.from_pretrained("google/timesfm-2.5-200m-transformers")model = model.to(torch.float32).eval()
past_values = [ torch.linspace(0, 1, 100), torch.sin(torch.linspace(0, 20, 67)),]
with torch.no_grad(): outputs = model(past_values=past_values, forecast_context_len=1024)
print(outputs.mean_predictions.shape)print(outputs.full_predictions.shape)@inproceedings{das2024a, title={A decoder-only foundation model for time-series forecasting}, author={Abhimanyu Das and Weihao Kong and Rajat Sen and Yichen Zhou}, booktitle={Forty-first International Conference on Machine Learning}, year={2024}, url={https://openreview.net/forum?id=jn2iTJas6h}}