Time series analysis plays a crucial role in practical applications such as traffic flow prediction, weather forecasting, electricity demand prediction, and stock market forecasting. Due to the presence of both long-term and short-term cyclical patterns in complex time series, previous research has predominantly focused on one-dimensional temporal domains, which poses significant challenges. The ability to capture cyclical variations is limited in one-dimensional temporal domains. To address this, we propose the Multi-Scale Temporal Correlation Multi-Dimensional Decomposition Network (MTCMD). Our approach transforms one-dimensional time series into multi-dimensional tensors that represent multiple long-term and short-term cycles. This multi-dimensional representation allows us to extract trend components and seasonal components more effectively. Moreover, in real-world scenarios, the interactions between time series cycles are dynamically changing and exhibit significant differences when observed at different temporal scales. Therefore, we introduce the Multi-Scale Temporal Correlation Learner to extract features of seasonal components at various scales, thereby enhancing our ability to learn the correlations of cyclical variations. Experimental results demonstrate that our proposed MTCMD model outperforms existing methods in mainstream time series analysis tasks. These results validate the rationality and effectiveness of transforming one-dimensional time series into multi-dimensional temporal domains.
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