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*This model was published in HF papers on 2023-10-14 and contributed to Hugging Face Transformers on 2026-02-27.*

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# TimesFM 2.5

## Overview

TimesFM 2.5 (Time Series Foundation Model) is a pretrained time-series foundation model proposed in [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688) by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. It builds on the original TimesFM architecture with rotary attention, QK normalization, per-dimension attention scaling, and continuous quantile prediction.

The abstract from the paper is the following:

*Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a decoder style attention model with input patching, using a large time-series corpus comprising both real-world and synthetic datasets. Experiments on a diverse set of previously unseen forecasting datasets suggests that the model can yield accurate zero-shot forecasts across different domains, forecasting horizons and temporal granularities.*

This model was contributed by [kashif](https://huggingface.co/kashif). The original code can be found [here](https://github.com/google-research/timesfm).

You can find the checkpoint at [`google/timesfm-2.5-200m-transformers`](https://huggingface.co/google/timesfm-2.5-200m-transformers).

## Usage example

```python
import numpy as np
import torch

from transformers import TimesFm2_5ModelForPrediction


model = TimesFm2_5ModelForPrediction.from_pretrained(
    "google/timesfm-2.5-200m-transformers",
    device_map="auto",
)

forecast_input = [
    np.sin(np.linspace(0, 20, 100)),
    np.sin(np.linspace(0, 20, 200)),
    np.sin(np.linspace(0, 20, 400)),
]
forecast_input_tensor = [torch.tensor(ts, dtype=torch.float32, device=model.device) for ts in forecast_input]

with torch.no_grad():
    outputs = model(past_values=forecast_input_tensor, return_dict=True)
    point_forecast = outputs.mean_predictions
    quantile_forecast = outputs.full_predictions
```

## TimesFm2_5Config

[[autodoc]] TimesFm2_5Config

## TimesFm2_5Model

[[autodoc]] TimesFm2_5Model
    - forward

## TimesFm2_5ModelForPrediction

[[autodoc]] TimesFm2_5ModelForPrediction
    - forward
