Rethinking multivariate modeling in long-term forecasting: an efficient univariate framework with power decomposition...

Long-term time series forecasting (LTSF) is critical to industrial applications. While recent advances mainly focus on modeling complex multivariate interactions, the practical benefits may only be marginal by challenges such as asynchronous data drifts, noise interference, and abrupt changes. This study demonstrates a well-designed univariate modeling can be more effective and efficient. The univariate model with Power Decomposition and online Post-Calibration (PDCNet) is proposed, which incorporates two novel mechanisms. 1) Power decomposition (P3D) is designed to disentangle time series based on data power distribution, which significantly enhancing data predictability and mitigating the obscuring effect from dominant periodicities. Predictability-ACF joint analysis is introduced to determine optimal decomposition thresholds. 2) By designing the abrupt factor M to classify the data morphological changes and historical performance-based correction dictionary, a lightweight online Post-Calibration is proposed to adapt to pattern drifts without retraining the main model. Comprehensive experiments show that PDCNet consistently outperforms state-of-the-art models on univariate tasks. Through simple aggregation, it also achieves top-tier multivariate performance. P3D brings an average 15% improvement in 68.57% of cases, while calibration further improves accuracy by 16% in calibrated regions. Notably, PDCNet reduces GPU memory usage which can reach up to 95% compared to multivariate counterparts in extreme-scale tasks. Our work proves that capturing internal temporal dependencies within each variable is a more efficient and practical design for LTSF. PDCNet can serve as a competitive baseline, offering superior performance with significantly reduced memory footprint. The related source code and configuration files can be accessed at https://github.com/hongchichen/PDCNet.