CrossWaveNet: A dual-channel network with deep cross-decomposition for Long-term Time Series Forecasting

Abstract

Many real-world applications require predicting data changes over a longer period, such as early warning for energy consumption and long-term planning for transportation. Long-term Time Series Forecasting (LTSF) demands models with high predictive capability. Recently, transformer-based models have achieved notable success in LTSF. However, the inherent nature of the permutation-invariant self-attention mechanism in Transformers can lead to the loss of certain temporal patterns. Therefore, we propose a dual-channel network with deep cross-decomposition for LTSF, called CrossWaveNet. In order to better capture long-term dependencies, unlike the complex network structure of Transformer, a simpler and more effective dual-channel network model for season and trend-cyclical is constructed. The original time series is decomposed into seasonal and trend-cyclical components simultaneously, and the dual-channel network structure is used to extract their features, respectively. This structure can improve the model’s generalization ability, which can have good results on various types of time series data. To effectively improve the accuracy of data decomposition, the moving average method is first used to obtain coarse-grained seasonal and trend-cyclical components, and then the extracted seasonal and trend-cyclical components are gradually merged into their respective channels through the dual-channel network with deep cross-decomposition, thereby obtaining fine-grained components and effectively improving prediction performance. Compared with the state-of-the-art methods in long-term forecasting, CrossWaveNet has achieved the highest prediction accuracy and significant relative performance improvement in fields such as energy, transportation, weather, and disease transmission.