MLPST: MLP is All You Need for Spatio-Temporal Prediction
Zijian Zhang Ze Huang Zhiwei Hu Xiangyu Zhao Wanyu Wang Zitao Liu Junbo Zhang S. Joe Qin Hongwei Zhao
Traffic prediction is a typical spatio-temporal data mining task and has
great significance to the public transportation system. Considering the demand
for its grand application, we recognize key factors for an ideal
spatio-temporal prediction method: efficient, lightweight, and effective.
However, the current deep model-based spatio-temporal prediction solutions
generally own intricate architectures with cumbersome optimization, which can
hardly meet these expectations. To accomplish the above goals, we propose an
intuitive and novel framework, MLPST, a pure multi-layer perceptron
architecture for traffic prediction. Specifically, we first capture spatial
relationships from both local and global receptive fields. Then, temporal
dependencies in different intervals are comprehensively considered. Through
compact and swift MLP processing, MLPST can well capture the spatial and
temporal dependencies while requiring only linear computational complexity, as
well as model parameters that are more than an order of magnitude lower than
baselines. Extensive experiments validated the superior effectiveness and
efficiency of MLPST against advanced baselines, and among models with optimal
accuracy, MLPST achieves the best time and space efficiency.