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 imputatio…

A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.

Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'

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

Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)

[CIKM'23] Official code for our paper "Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting".

Diff-DGMN: A Diffusion-based Dual Graph Multi-attention Network for POI Recommendation

A pytorch re-implementation of STFGNN

A Collaborative Trajectory Representation model (CTRNext) to enhance the next POI recommendation

The project for KDD24 paper 'Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks'

The official source code of MTSCI: A Conditional Diffusion Model for Consistent Imputation in Incomplete Time Series