EAST: Enhanced AutoML for Saptio-Temporal Data

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Huge spatio-temporal data (e.g., traffic flow, human mobility, and geographical data) are generated in modern cities. Accurately forecasting over spatio-temporal data enables many essential applications in intelligent cities, such as traffic management, public safety, and economy. In this project, we study automated machine learning techniques (e.g. neural architecture search) for spatio-temporal data. To improve the ease of use, we propose an Enhanced Automated machine learning library for Spatio-Temporal forecasting, entitled EAST. In EAST, we mainly reconstruct three types of automated machine learning methods, namely, AutoSTPoint, AutoSTGrid, and AutoSTGraph for spatio-temporal point (STPoint) forecasting, spatio-temporal grid (STGrid) forecasting, and spatiotemporal graph (STGraph) forecasting, respectively.

Senior Researcher

My research interests include deep learning, data mining, AI, big data analytics, and urban computing.

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