Forecasting complex time series is ubiquitous and vital in a range of applications but challenging. Recent advances endeavor to achieve progress by incorporating various deep learning techniques (e.g., RNN and Transformer) into sequential models. However, clear patterns are still hard to extract since intricate time series are composed of several entangled components. Motivated by the success of disentangled variational autoencoder in computer vision and classical time series decomposition, we p…