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*This model was released on 2023-10-14 and added to Hugging Face Transformers on 2025-04-16.*

# TimesFM

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

TimesFM (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 is a decoder only model that uses non-overlapping patches of time-series data as input and outputs some output patch length prediction in an autoregressive fashion.

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 patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths 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).

To use the model:

```python
import numpy as np
import torch
from transformers import TimesFmModelForPrediction


model = TimesFmModelForPrediction.from_pretrained(
    "google/timesfm-2.0-500m-pytorch",
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
    device_map="auto"
)


 # Create dummy inputs
forecast_input = [
    np.sin(np.linspace(0, 20, 100)),
    np.sin(np.linspace(0, 20, 200)),
    np.sin(np.linspace(0, 20, 400)),
]
frequency_input = [0, 1, 2]

# Convert inputs to sequence of tensors
forecast_input_tensor = [
    torch.tensor(ts, dtype=torch.bfloat16).to(model.device)
    for ts in forecast_input
]
frequency_input_tensor = torch.tensor(frequency_input, dtype=torch.long).to(model.device)

# Get predictions from the pre-trained model
with torch.no_grad():
    outputs = model(past_values=forecast_input_tensor, freq=frequency_input_tensor, return_dict=True)
    point_forecast_conv = outputs.mean_predictions.float().cpu().numpy()
    quantile_forecast_conv = outputs.full_predictions.float().cpu().numpy()
```

## TimesFmConfig

[[autodoc]] TimesFmConfig

## TimesFmModel

[[autodoc]] TimesFmModel
    - forward

## TimesFmModelForPrediction

[[autodoc]] TimesFmModelForPrediction
    - forward
