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
