McWC introduces a model that separately captures cyclicity, trend, and inter-channel correlations in long-term time series forecasting. It uses multi-layer cyclicity construction, wavelet decomposition, and a multi-layer perceptron to extract and fuse high- and low-frequency information, while decoupling intra-channel autocorrelations via frequency-domain loss. Experiments on six real-world datasets show McWC achieves state-of-the-art performance with high computational efficiency.