COMPASS: Channel-wise Calibration of Periodic Priors for Multivariate Time Series Forecasting

Multivariate time series forecasting requires effectively leveraging both historical periodic patterns and current dynamics. Existing periodic forecasting methods typically employ a uniform strategy to incorporate historical periodic priors across channels, overlooking the channel-wise differences between historical priors and current dynamics. Such uniform fusion can introduce mismatched periodic information into channels where historical priors differ more from current dynamics. To address this issue, we propose COMPASS, a channel-wise periodic prior adaptation framework for multivariate time series forecasting. COMPASS focuses on the channel-wise inconsistency between historical periodic priors and current dynamics and first performs channel-wise relationship calibration to quantify this inconsistency and adaptively adjust their contributions across channels. To further exploit useful periodic information, COMPASS employs dynamic-guided prior enhancement, which aggregates periodic information across channels according to the current dynamics. The enhanced periodic prior is then adaptively fused with current dynamics for forecasting. Extensive experiments on 12 public forecasting benchmarks demonstrate the effectiveness of COMPASS, reducing MSE by an average of 2.23% over the strongest competing methods. COMPASS achieves the best performance on 6 of 8 long-term forecasting datasets and all 4 short-term forecasting datasets.