🤖 AI Summary
This work addresses the limitations of existing long-term time series forecasting methods, which often neglect inter-channel correlations and suffer from high model complexity and low computational efficiency. The authors propose an efficient and accurate forecasting framework that captures multi-level periodic patterns through multi-scale periodic modeling, explicitly models inter-channel dependencies using multi-layer perceptrons, and employs multi-level wavelet decomposition to separate trend and periodic components. Additionally, a frequency-domain loss function is introduced to decouple intra-channel autocorrelations. Evaluated on six real-world datasets, the proposed method achieves state-of-the-art performance, significantly improving prediction accuracy while maintaining superior computational efficiency and strong capability in extracting historical information.
📝 Abstract
Cyclicity and trend are important components of time series data and many studies based on cyclicity and trend have achieved good results in long-term time series forecasting. However, we believe that current work neglects the influence of real-world inter-channel correlations in time series data which leads to suboptimal predictions. Furthermore, these models rely on complex designs to capture diverse information so that resulting in low computational efficiency. To address this challenge, we propose McWC, a long-term time series forecasting model that separately models the cyclicity, trend, and inter-channel correlations. Specifically, McWC first decouples cyclical information from data using a multi-layer cyclicity construction module. Then, it extracts inter-channel correlations using multi-layer perceptron. Next, it models and fuses the multi-layer high-frequency and low-frequency information from data using a multi-level wavelet decomposition module. Finally, it aggregates the results of different components to obtain the output. Simultaneously, we decouple intra-channel autocorrelations by calculating a loss function in the frequency domain. Experiments on six real-world datasets demonstrate that McWC achieves state-of-the-art performance, exhibiting excellent computational efficiency and historical information extraction capabilities.