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Timer⏱️

This repo provides official code and checkpoints for Timer: Transformers for Time Series Analysis at Scale.

Updates

🚩 News (2024.3) Checkpoint pre-trained on UTSD-4G is available.

🚩 News (2024.3) We provide the downstream fine-tune code for the forecasting task.

Introduction

Time Series Transformer (Timer) includes GPT-style Transformers pre-trained on multi-domain time series as Large Time Series Model (LTSM).

We curate large-scale datasets comprised of 1B time points, proposing a unified training strategy with single-series sequence, and presenting Timer with the decoder-only architecture. As a LTSM, Timer is enabled with:

  • Generalization ability that one model fits all domains.

  • Task generality that one model copes with various tasks.

  • Scalability that the performance increases with the scale of pre-training.

Usage

  1. Install Pytorch and necessary dependencies.
pip install -r requirements.txt

  1. The datasets can be obtained from Google Drive or Tsinghua Cloud.

  2. Download the pre-trained checkpoints:

  3. Train and evaluate the model. We provide the above tasks under the folder ./scripts/.

# forecasting
bash ./scripts/forecast/ECL.sh

# TODO: segement-level imputation
bash ./scripts/imputation/ECL.sh

# TODO: anomaly detection on the fly
bash ./scripts/anomaly_detection/UCR.sh

Showcases

Forecasting with data scarcity (limited downstream training samples)

Segment-level imputation with few-shot samples

On-the-fly anomaly detection on UCR Anomaly Archive

Approach

Large Dataset

We curate Unified Time Series Dataset (UTSD) that includes 7 domains with up to 1 billion time points with hierarchical capacities to facilitate research of scalability and domain transfer.

Pre-training Strategy

To facilitate pre-training on extensive time series, we convert heterogeneous series into single-series sequence (S3), reserving the patterns of series variations with the unified context length, towards the well-established tokenization like natural language.

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Model Architecture

With the substantial progress of decode-only large language models and evaluation of other backbone alternatives, we adopt the GPT-style Transformer with autoregressive generation towards LTSMs.

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Unified Generative Task Formulation

Timer is applicable on various tasks, which is realized in the unified generative approach.

Task Formulation
Time Series Forecasting Next Token Prediction
Imputation (Segment-level) Denoising Autoencoding
Anomaly Detection (Predictive) Next Token Prediction

Performance

We compare Timer with state-of-the-art approaches and present the pre-training benefit on data-scarce scenarios, known as the few-shot cpability of large models.

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Scalability

By increasing the parameters and pre-training scale, Timer achieves notable performance improvement: 0.231 $\to$ 0.138 (−40.3%), surpassing the previous state-of-the-art deep forecasters.

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Flexible Sequence Length

The decoder-only architecture provides additional flexibility to accommodate series of different lookback and forecast lengths.

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Future work

We are preparing to release the composition of datasets (UTSD), larger checkpoints, and code for pre-training. Please stay tuned for the update!

Citation

If you find this repo helpful, please cite our paper.

@article{liu2024timer,
  title={Timer: Transformers for Time Series Analysis at Scale},
  author={Liu, Yong and Zhang, Haoran and Li, Chenyu and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
  journal={arXiv preprint arXiv:2402.02368},
  year={2024}
}

Contact

If you have any questions or want to use the code, feel free to contact: