Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types of probablistic models.

missCompare R package - intuitive missing data imputation framework

Solve many kinds of least-squares and matrix-recovery problems

implementation of a Deep Kernelized Auto Encoder for learning vectorial representations of mutlivariate time series with missing data.

Code accompanying the notMIWAE paper

An implementation to Convolutional generative adversarial imputation networks for spatio-temporal missing data Nets Paper (Conv-GAIN)

MLimputer: Missing Data Imputation Framework for Machine Learning

Repository for the semester project "Sensor-Based Modeling of Fatigue Using Transformer Model" at ETH AI Center (Fall semester 2022)

Project page for EUSIPCO 2022 paper 'Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals'

A library for synthetic missing data generation.

Official implementation of 'Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis' (MaCoDE) with pytorch (AAAI 2025 accepted paper).

Power Outage Data Analysis in USA

Approximated missing values in noisy, heterogeneous electronic health records by low rank modeling.

Numerical data imputation methods for extremely missing data contexts

A comprehensive guide to mastering Pandas for data analysis, featuring practical examples, real-world case studies, and step-by-step tutorials. For general information, see

Multi-class classification model to predict outcomes of cirrhosis patients using machine learning

Cleans and validates raw data against predefined rules

Implementation of Missing Imputation algorithms for Incomplete tabular data with PyTorch.

End-to-End Python implementation of Mo et al.'s (2025) ACT-Tensor methodology; a tensor completion framework for financial dataset imputation. Implements cluster-based CP decomposition, HOSVD factor extraction, temporal smoothing (CMA/EMA/Kalman), and downstream asset pricing evaluation. Transforms sparse data into dense machine readable data.