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

Abstract

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 due to memory bottlenecks. To address these challenges, we present MiniST, a minimalist framework that decouples information content from sequence length by exploiting the intrinsic redundancy of traffic data. Instead of modeling dense point-wise dependencies, MiniST projects high-dimensional traffic history into a compact set of Orthogonal Basis Vectors, effectively distilling dominant traffic modes into a disentangled latent space. Furthermore, we introduce a Template-based Update Module (TUM), which models temporal dynamics by anchoring these latent bases to a set of learnable, time-invariant global templates. Extensive experiments on multiple large-scale datasets, including a network with over 50,000 nodes, demonstrate that MiniST achieves state-of-the-art performance in both short-term and long-term traffic forecasting, outperforming 22 benchmark models with improvements of up to 11.58%, while maintaining minimal parameter size and competitive efficiency.The source code is available at MiniST Repository.