Yang Wang

MiniST: Unlocking Input Window Length in Traffic Flow Forecasting with Compact Parameters

Spatiotemporal traffic forecasting currently faces dual challenges: capturing long-range periodic dependencies and managing the computational burden of increasingly complex deep neural network architectures. Mainstream models typically contain millions of parameters and struggle to handle long sequence historical data...

StreamMTS: Towards Streaming Multivariate Time Series Forecasting

Current mainstream research in multivariate time series (MTS) forecasting often assumes that all data are static. However, real-world MTS data typically arrives continuously in a streaming manner, which we refer to as streaming MTS. The statistical characteristics and spatiotemporal graph topology of these data evolve...

MiniST: Unlocking Input Horizon Bottleneck in Traffic Forecasting with Compact Parameters

MADGCN: A Meteorology-Aware Spatio-Temporal Graph Convolution Network for Long-Term Air Pollution Forecasting

Air quality forecasting has attracted increasing attention as global air pollution worsens. Spatiotemporal graph neural networks have become a leading paradigm, thanks to their ability to capture complex spatial and temporal dynamics in Air Quality Index (AQI) data. However, existing methods remain limited by weak mode...