Graph-based mutually exciting point processes

Mutually exciting point process graphs for modelling dynamic networks

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

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Python client library for Google Maps API Web Services

Python bindings for H3, a hierarchical hexagonal geospatial indexing system

Graph Convolutional Adversarial Networks for Spatio-Temporal Anomaly Detection

Useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories.

Some TrafficFlowForecasting Solutions(交通流量预测解决方案)

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

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