Time series data forecasting method based on spatio-temporal dimensions reconstruction

To address insufficient modeling of inter-variable dependencies and suboptimal adaptation to spatiotemporal dynamics in multivariate time series forecasting, this paper proposes a Transformer network, STARFormer, incorporating spatiotemporal dimension reconstruction． This approach utilizes a segmented encoding mechanism that transforms single-dimensional temporal information into 2D vector matrices through dimension inversion． A dual-phase attention architecture is developed to hierarchically capture cross-temporal and cross-dimensional dependencies, effectively strengthening temporal representation learning． Further, this work introduces a dynamic graph module to model evolving dependencies between temporal patterns and spatial structures． Experimental evaluations across five real-world datasets demonstrate that STARFormer outperforms state-of-the-art Transformer-based models in multivariate forecasting tasks．