Jia-Ming Ma

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