repo_description: The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516
repo_topics: attention attention-mechanism deep-learning imputation imputation-model impute incomplete-data incomplete-time-series interpolation irregular-sampling machine-learning missing-values partially-observed partially-observed-data partially-observed-time-series pytorch self-attention time-series time-series-imputation transformer
code: """
  This file stores RANDOM_SEED for global configuration.
  
  If you use code in this repository, please cite our paper as below. Many thanks.
  
  @article{DU2023SAITS,
  title = {{SAITS: Self-Attention-based Imputation for Time Series}},
  journal = {Expert Systems with Applications},
  volume = {219},
  pages = {119619},
  year = {2023},
  issn = {0957-4174},
  doi = {https://doi.org/10.1016/j.eswa.2023.119619},
  url = {https://www.sciencedirect.com/science/article/pii/S0957417423001203},
  author = {Wenjie Du and David Cote and Yan Liu},
  }
  
  or
  
  Wenjie Du, David Cote, and Yan Liu. SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023. https://doi.org/10.1016/j.eswa.2023.119619
  
  """
  
  # Created by Wenjie Du <[REDACTED_EMAIL]>
  # License: MIT
  
  
  RANDOM_SEED = 26
  