TSDNet: A two-stage decomposition-based hybrid deep neural network for long-term time series forecasting

Abstract

Due to recent developments in deep learning models, the field of time series forecasting has undergone significant advancements related to forecasting accuracy and reliability across various industrial sectors. Unfortunately, traditional deep learning models often encounter long-term and multivariate forecasting challenges due to complex temporal patterns. To overcome this, decomposition-based approaches have been proposed. However, there have been few attempts to utilize an appropriate network type for each decomposed component. In this paper, we propose the two-stage decomposition-based hybrid deep neural network (TSDNet) for enhancing the accuracy of long-term time series forecasting. To effectively manage complicated time series data with varying periodicities, TSDNet accommodates a single linear layer for forecasting smooth trend components and a convolutional module for complex seasonal components. Extensive experiments on various benchmark and real-world financial datasets show that TSDNet mostly improves the forecasting accuracy compared to the existing methods considered, particularly in long-term forecasting scenarios. Furthermore, ablation studies were conducted to examine the impact of the number of decomposition stages and the implementation of different modules on the decomposed elements, suggesting the effectiveness of the proposed approach.