PHAT：用于多变量时间序列预测的周期异质性建模
PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting
AI总结：
PHAT通过周期异质性感知机制，有效捕捉多变量时间序列中的周期差异，提升预测性能。
摘要：
尽管现有的多变量时间序列预测模型在建模周期性方面取得了显著进展，但它们大多忽略了现实数据中普遍存在的周期异质性，其中变量表现出不同且动态变化的周期。为有效捕捉这种周期异质性，我们提出了PHAT（Period Heterogeneity-Aware Transformer）。具体而言，PHAT将多变量输入组织成一个三维
英文摘要：
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 perspectives: periodic alignment and periodic deviation. Additionally, the periodic alignment attention scores are decomposed into positive and negative components, with a modulation term encoding periodic priors. This modulation constrains the attention mechanism to more faithfully reflect the underlying periodic trends. A mathematical explanation is provided to support this property. We evaluate PHAT comprehensively on 14 real-world datasets against 18 baselines, and the results show that it significantly outperforms existing methods, achieving highly competitive forecasting performance. Our sources is available at GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
