DeRNN: Decomposed Recurrent Neural Network for Long-Term Time Series Forecasting

Abstract

Long-Term Time Series Forecasting (LTSF) is pivotal in domains like energy and traffic management but necessitates capturing intricate dependencies over extended windows. While Transformer-based models dominate, they suffer from quadratic complexity and positional insensitivity. Conversely, recent lightweight MLP/RNN-based models often forcibly compress conflicting dynamic features—linear trends and non-linear fluctuations—into a single channel, leading to suboptimal accuracy. To address these limitations, we propose the Decomposed Recurrent Neural Network (DeRNN). Our approach decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture. Specifically, we introduce a Trend Anchor Track to preserve global scale via direct linear projection, and a Seasonal Feature Track utilizing Bi-directional GRUs to capture complex non-linear dependencies within a reversible normalized space. Extensive experiments on seven benchmarks demonstrate that DeRNN achieves highly competitive, and in most cases superior, accuracy against state-of-the-art methods while maintaining extremely low latency and memory usage. Furthermore, the model exhibits superior robustness against noise and distribution shifts.