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Time series analysis has found widespread applications in areas such asweather forecasting, anomaly detection, and healthcare. However, real-worldsequential data often exhibit a superimposed state of various fluctuationpatterns, including hourly, daily, and monthly frequencies. Traditionaldecomposition techniques struggle to effectively disentangle these multiplefluctuation patterns from the seasonal components, making time series analysischallenging. Surpassing the existing multi-period decoupling paradigms, thispaper introduces a novel perspective based on energy distribution within thetemporal-spectrum space. By adaptively quantifying observed sequences intocontinuous frequency band intervals, the proposed approach reconstructsfluctuation patterns across diverse periods without relying on domain-specificprior knowledge. Building upon this innovative strategy, we propose Pets, anenhanced architecture that is adaptable to arbitrary model structures. Petsintegrates a Fluctuation Pattern Assisted (FPA) module and a Context-GuidedMixture of Predictors (MoP). The FPA module facilitates information fusionamong diverse fluctuation patterns by capturing their dependencies andprogressively modeling these patterns as latent representations at each layer.Meanwhile, the MoP module leverages these compound pattern representations toguide and regulate the reconstruction of distinct fluctuations hierarchically.Pets achieves state-of-the-art performance across various tasks, includingforecasting, imputation, anomaly detection, and classification, whiledemonstrating strong generalization and robustness.

Pets: General Pattern Assisted Architecture For Time Series Analysis