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partially-observed-time-series

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PyPOTS

WenjieDu / PyPOTS

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Star 685

A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science analysis tasks of imputation, classification, clustering, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate TS with NaN missing values

data-science machine-learning data-mining deep-learning time-series neural-network clustering interpolation pytorch healthcare forecasting classification incomplete imputation missing-data missingness time-series-analysis missing-values partially-observed-time-series reality-centric
  • Updated May 9, 2024
  • Python
SAITS

WenjieDu / SAITS

Sponsor
Star 265

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

machine-learning deep-learning time-series interpolation pytorch transformer imputation attention attention-mechanism irregular-sampling incomplete-data imputation-model missing-values self-attention impute time-series-imputation partially-observed-data partially-observed partially-observed-time-series incomplete-time-series
  • Updated Apr 25, 2024
  • Python

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