这篇论文用McWC模型同时处理周期、趋势和通道相关性，在六个数据集上最准，算得还快，值得看。
McWC模型分别建模周期、趋势和跨通道相关性。其多层循环性构建模块解耦循环信息，多层感知机提取通道间相关性，多级小波分解融合高低频信息。在六个真实世界数据集上达到最优性能，计算效率优于此前方法。
Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting
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.