PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Data visualization examples and tutorials from scratch. 数据可视化示例代码集与新手学习教程。

tsl: a PyTorch library for processing spatiotemporal data.

Traffic data processing tools in LibCity

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

A collection of research on spatio-temporal data mining

Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.

A Python package for simple STAC queries

Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data

A framework for Spatio-Temporal Data Analytics on Spark

Trajectory Mining Library

[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"

PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

Simple and effective tools for the analysis of movement data

[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"

ST-OPTICS: Spatio-temporal clustering based on OPTICS

Graph Neural Networks utilization for Spatiotemporal graphs. These methods will be applied into the problem of forecasting traffic flow on PEMS-Bay, METR-LA and Seattle Loop Datasets