RDLinear: A Novel Time Series Forecasting Model Based on Decomposition with RevIN

Time series forecasting, with its wide range of practical applications such as power load and weather prediction, has become a pivotal field of research. Over the past few years, neural network models have made remarkable progress in this domain. Many time series forecasting models now employ sequence decomposition techniques to enhance forecasting accuracy, including Autoformer, DLinear, and MICN. These techniques break down the original time series data into two components: trend and seasonal term, to facilitate more accurate predictions. However, a significant limitation of existing models that utilize sequence decomposition is their incomplete exploitation of the trend component. To address this issue, we introduce RDLinear, a structurally simple model designed to fully leverage the unique attributes of sequence decomposition. RDLinear employs distinct forecasting strategies, with a primary focus on utilizing the RevIN method to predict the trend component. In this paper, we present extensive experimental results on multiple real-world datasets. Our findings demonstrate that RDLinear outperforms other time-series forecasting models, particularly in long-term forecasting. Furthermore, ablation experiments confirm the effectiveness of our proposed method.