tsl: a PyTorch library for processing spatiotemporal data.
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Jul 27, 2026 - Python
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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)
Bayesian Neural Field models for prediction in large-scale spatiotemporal datasets
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)
[KDD 2022] SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data
Attention Feature Fusion base on spatial-temporal Graph Convolutional Network(AFFGCN)
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
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.
BSTVC-R is an R package for spatiotemporal heterogeneous analysis within a unified full-map framework. It supports the analysis of locally varying influencing factors, identification of key global drivers, and dynamic prediction, providing a programmable and reproducible workflow for spatiotemporal interpretable modeling.
The official repository for "Unveiling the Role of Climate in Spatially Synchronized Locust Outbreak Risks"
CellTRIP, Inferring virtual cell environments using multi-agent reinforcement learning for spatiotemporal trajectory interpolation, imputation, and perturbation
An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution
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.
Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.
SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena
Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now.
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.
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