pypots
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/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
@stdlib/strided-base-mskunary
Apply a unary callback to elements in a strided input array according to elements in a strided mask array and assign results to elements in a strided output array.
@stdlib/strided-napi-mskunary
C API for registering an N-API module exporting a strided array interface for applying a unary callback to an input strided array according to a mask strided array.
JointAI
Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values
https://github.com/exascaleinfolab/imputegap
ImputeGAP: A library of Imputation Techniques for Time Series Data
pygrinder
PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data simulation 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
predicting-missing-pairwise-preferences-in-gdm
Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system
saits
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