MSI-Multi-Step Interaction Networks for Spatial-Temporal Forecasting

Author(s)	Shiva Nidamanuri, Prasanth Tirumalasetty, Navya Sree Kilari, Jin Lu
Country	United States
Abstract	Spatial-temporal (ST) forecasting is a critical but inherently challenging task in real-world applications such as traffic prediction and urban monitoring1. Existing models often struggle to capture complex, long-range dependencies that vary across different locations and time steps2. This paper introduces MSDR: Multi-Step Dependency Relation Networks, a novel framework designed to address these challenges. MSDR explicitly utilizes hidden states from multiple previous time steps, employing a "dependency relation operation" within a GMSDR (Graph Multi-Step Dependency Relation) block to learn high-quality representations3. This block is integrated into an Encoder-Decoder architecture 4and uses graph convolutions and attention mechanisms to model both spatial and temporal dependencies5. We evaluated our model on four public benchmark datasets (NYC Citi Bike, NYC Taxi, PEMS03, and PEMS08). The experimental results demonstrate that GMSDR achieves state-of-the-art performance, consistently outperforming established baselines like DCRNN, STGCN, and GraphWaveNet on all key metrics (RMSE, MAE, and MAPE%)6666.
Field	Engineering
Published In	Volume 14, Issue 2, April-June 2023
Published On	2023-06-09

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International Journal on Science and Technology