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

Graph Neural Networks for Irregular Time Series

A Julia implementation of basic tools for time series analysis compatible with incomplete data.

Dynamic Time Warping

Converting irregularly spaced time series, such as eletronic health records, into dataframes for tabular classification.

Elastic-net VARMA: hyperparameter optimisation, estimation and forecasting

Pytorch implementation of "Multi-view Integration Learning for Irregularly-sampled Clinical Time Series" (Under review, JBHI)

[Financial Innovation] The official repo for the paper: "CryptMAGE: Vision Transformer for Intraday Cryptocurrency Time Series Price Movements Recognition".

Research on deep learning models to handle irregular time series water quality data. Data are collected from USGS, DbHydro databases. Data can be loosely termed as irregularly regular.