DPHM-Net:de-redundant multi-period hybrid modeling network for long-term series forecasting

Abstract

Deep learning models have been widely applied in the field of long-term forecasting has achieved significant success, with the incorporation of inductive bias such as periodicity to model multi-granularity representations of time series being a commonly employed design approach in forecasting methods. However, existing methods still face challenges related to information redundancy during the extraction of inductive bias and the learning process for multi-granularity features. The presence of redundant information can impede the acquisition of a comprehensive temporal representation by the model, thereby adversely impacting its predictive performance. To address the aforementioned issues, we propose a De-Redundant Multi-Period Hybrid Modeling Network (DPHM-Net) that effectively eliminates redundant information from the series inductive bias extraction mechanism and the multi-granularity series features in the time series representation learning. In DPHM-Net, we propose an efficient time series representation learning process based on a period inductive bias and introduce the concept of de-redundancy among multiple time series into the representation learning process for single time series. Additionally, we design a specialized gated unit to dynamically balance the elimination weights between series features and redundant semantic information. The advanced performance and high efficiency of our method in long-term forecasting tasks against previous state-of-the-art are demonstrated through extensive experiments on real-world datasets.