A Multiscale Collaborative Learning Method on Long-Term Time Series for Cyber-Physical-Social Intelligence

Cyber-physical-social system (CPSS) is an interactive system that combines digital technologies, physical infrastructures, and social dynamics. Within the CPSS framework, coordination of data flows, information flows, and knowledge flows facilitates seamless collaboration across human–machine boundaries. With the development and integration of artificial intelligence, cyber-physical-social intelligence (CPSI) has emerged, facilitating intelligent decision-making and promoting adaptive behavior in dynamic environments. CPSI aims to provide personal services through efficient CPSS data processing. Long-term time series (LTS), as one of the critical CPSS data, play a vital role in enabling CPSI to anticipate and address potential problems or changes in demand. However, real-world series commonly present irregular temporal patterns, which complicates the analysis of global information. Single-scale periodicity typically limits the exploration of temporal patterns, limiting the baseline’s ability to identify dynamic patterns. Moreover, LTS are frequently affected by noise, and excessively long sequences may hinder models from effectively capturing intersequence dependencies and the deep structural features embedded in the data. To address these challenges, this article proposes multiscale decomposition analysis baseline (MDAB), a highly effective long-term time series forecasting (LTSF) model. MDAB extracts multiscale components and performs collaborative learning of information across multiscales. For trend components, it integrates complex frequency-domain optimization to reduce noise, while for seasonal components, it employs efficient sparse attention to deeply analyze features and reduce memory usage. Comprehensive experiments carried out on several benchmark LTS datasets demonstrate that MDAB exceeds most existing LTSF models.