[2602.00654] PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting

Computer Science > Machine Learning arXiv:2602.00654 (cs) [Submitted on 31 Jan 2026 (v1), last revised 2 Mar 2026 (this version, v3)] Title:PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting Authors:Jiaming Ma, Qihe Huang, Haofeng Ma, Guanjun Wang, Sheng Huang, Zhengyang Zhou, Pengkun Wang, Binwu Wang, Yang Wang View a PDF of the paper titled PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting, by Jiaming Ma and Qihe Huang and Haofeng Ma and Guanjun Wang and Sheng Huang and Zhengyang Zhou and Pengkun Wang and Binwu Wang and Yang Wang View PDF HTML (experimental) Abstract:While existing multivariate time series forecasting models have advanced significantly in modeling periodicity, they largely neglect the periodic heterogeneity common in real-world data, where variables exhibit distinct and dynamically changing periods. To effectively capture this periodic heterogeneity, we propose PHAT (Period Heterogeneity-Aware Transformer). Specifically, PHAT arranges multivariate inputs into a three-dimensional "periodic bucket" tensor, where the dimensions correspond to variable group characteristics with similar periodicity, time steps aligned by phase, and offsets within the period. By restricting interactions within buckets and masking cross-bucket connections, PHAT effectively avoids interference from inconsistent periods. We also propose a positive-negative attention mechanism, which captures periodic dependencies from two persp...