86 lines
3.4 KiB
Markdown
86 lines
3.4 KiB
Markdown
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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-->
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*This model was released on 2023-10-14 and added to Hugging Face Transformers on 2025-04-16.*
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# TimesFM
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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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.
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The abstract from the paper is the following:
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*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.*
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This model was contributed by [kashif](https://huggingface.co/kashif).
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The original code can be found [here](https://github.com/google-research/timesfm).
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To use the model:
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```python
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import numpy as np
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import torch
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from transformers import TimesFmModelForPrediction
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model = TimesFmModelForPrediction.from_pretrained(
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"google/timesfm-2.0-500m-pytorch",
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dtype=torch.bfloat16,
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attn_implementation="sdpa",
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device_map="auto"
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)
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# Create dummy inputs
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forecast_input = [
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np.sin(np.linspace(0, 20, 100)),
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np.sin(np.linspace(0, 20, 200)),
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np.sin(np.linspace(0, 20, 400)),
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]
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frequency_input = [0, 1, 2]
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# Convert inputs to sequence of tensors
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forecast_input_tensor = [
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torch.tensor(ts, dtype=torch.bfloat16).to(model.device)
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for ts in forecast_input
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]
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frequency_input_tensor = torch.tensor(frequency_input, dtype=torch.long).to(model.device)
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# Get predictions from the pre-trained model
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with torch.no_grad():
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outputs = model(past_values=forecast_input_tensor, freq=frequency_input_tensor, return_dict=True)
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point_forecast_conv = outputs.mean_predictions.float().cpu().numpy()
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quantile_forecast_conv = outputs.full_predictions.float().cpu().numpy()
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```
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## TimesFmConfig
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[[autodoc]] TimesFmConfig
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## TimesFmModel
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[[autodoc]] TimesFmModel
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- forward
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## TimesFmModelForPrediction
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[[autodoc]] TimesFmModelForPrediction
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- forward
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