TimesFM 2.5 (Transformers)
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:
- Original model: google/timesfm-2.5-200m-pytorch
- Paper: A decoder-only foundation model for time-series forecasting
- Transformers docs: TimesFM 2.5
Model description
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:
- patch-based inputs for time-series contexts
- decoder-only self-attention stack
- point and quantile forecasts
Usage (Transformers)
import torch
from 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)
Conversion details
This checkpoint was produced with:
- script:
src/transformers/models/timesfm_2p5/convert_timesfm_2p5_original_to_hf.py - source checkpoint:
google/timesfm-2.5-200m-pytorch - conversion date (UTC):
2026-02-20
Weight conversion parity is verified by comparing converted-model forecasts against the official implementation outputs on deterministic inputs.
Citation
@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}
}