tsl: a PyTorch library for processing spatiotemporal data.

PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

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

Code for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting" (Neurocomputing, Elsevier)

Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 2022)

Given a shapefile with time-annotated vector objects (e.g., building footprints + construction year), this script will automatically create an animated GIF illustrating the dynamics for a user-specified period of time

Attention Feature Fusion base on spatial-temporal Graph Convolutional Network（AFFGCN）

Estimation, analysis and decomposition of brainwave spatiotemporal dynamics

Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.

[KDD 2022] Official implementation of "SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data".

Bayesian Neural Field models for prediction in large-scale spatiotemporal datasets

[KDD 2023] Official implementation of "Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM".

The official repository for "Unveiling the Role of Climate in Spatially Synchronized Locust Outbreak Risks"

This repository introduces Deep Particulate Matter Network with a Separated Input model based on deep learning by using ConvGRU, which can simultaneously analyze spatiotemporal information to consider the diffusion of particulate matter.

An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.

Spatiotemporal variability in the association between mental illness and substance use mortality and unemployment in the contiguous US

SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena

Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.

SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena