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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
Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Transformer architectures for irregulary sampled time series classification
Toolbox for interpolating from an uneven grid
Gaussian Process Inference