This repository contains the official implementation of the paper: NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining, which is accepted by TMLR 2024. In this work, we propose the NuTime model for large-scale time series pretraining. The model is based on the Transformer architecture, which takes input as a set of tokens from non-overlapping windows. Each window is represented by its normalized shape, the window mean and the window standard deviation. We develop a numerically multi-scaled embedding method (NME) for representing the scalar values of mean and std. The model can take raw values of time-series data as input without any data normalization and transformation.

Feel free to contact me ([email protected]) or open an issue if you have any questions or suggestions.

• 2024-07-10: NuTime is accepted by TMLR 2024.

• Release the training and evaluation code
• Release the self-supervised pretrained NuTime
• Release the large-scale merged datasets for pretraining

If you find our work helpful, please consider citing:

@article{lin2024nutime,
  title={NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining},
  author={Chenguo Lin and Xumeng Wen and Wei Cao and Congrui Huang and Jiang Bian and Stephen Lin and Zhirong Wu},
  journal={Transactions on Machine Learning Research (TMLR)},
  year={2024}
}