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.

翻译：周期性和趋势是时间序列数据的重要组成部分，许多基于周期性和趋势的研究已在长期时间序列预测中取得良好效果。然而，我们认为当前工作忽视了真实世界中时间序列数据通道间相互关联的影响，这导致了次优预测结果。此外，这些模型依赖复杂设计来捕获多样化信息，导致计算效率低下。针对这一挑战，我们提出McWC，一种分别建模周期性、趋势和通道间相关性的长期时间序列预测模型。具体而言，McWC首先通过多层周期性构建模块从数据中解耦周期性信息；随后利用多层感知器提取通道间相关性；接着通过多级小波分解模块对数据中的多层高频与低频信息进行建模与融合；最后聚合不同组件的输出结果。同时，我们通过在频域中计算损失函数来解耦通道内自相关性。在六个真实世界数据集上的实验表明，McWC达到了最先进的性能，展现出优异的计算效率与历史信息提取能力。