Gary Tan — accepted AI-conference papers

2025

Distribution-Aware Online Learning for Urban Spatiotemporal Forecasting on Streaming Data

Chengxin Wang, Gary Tan, Swagato Barman Roy, Beng Chin Ooi

IJCAI 2025

The intrinsic non-stationarity of urban spatiotemporal (ST) streams, particularly unique distribution shifts that evolve over time, poses substantial challenges for accurate urban ST forecasting. Existing works often overlook these dynamic shifts, limiting their ability to adapt to evolving trends e

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2025

Investigating Pattern Neurons in Urban Time Series Forecasting

Chengxin Wang, Yiran Zhao, Shaofeng Cai, Gary Tan

ICLR 2025poster

Urban time series forecasting is crucial for smart city development and is key to sustainable urban management. Although urban time series models (UTSMs) are effective in general forecasting, they often overlook low-frequency events, such as holidays and extreme weather, leading to degraded performa…

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