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A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation, classification, clustering, forecasting, & anomaly detection on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
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
a Python toolbox loads 172 public time series datasets for machine learning/deep learning with a single line of code. Datasets from multiple domains, e.g. healthcare, financial, power, traffic, and weather.
Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data
A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.
A Python kit corrupts time series into the incomplete by introducing missing values with different missingness patterns, including MCAR (complete at random), MAR (at random) , MNAR (not at random), sub sequence missing, and block missing.
PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series".