SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

With the proliferation of the Internet of Things (IoT) applications, advanced time-series forecasting techniques have become increasingly critical for managing and responding to complex temporal dynamics. However, traditional recurrent neural network (RNN)-based methods have faced challenges in the long-term time-series forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons. Consequently, the dominance in this domain has shifted toward Transformer, multilayer perceptron (MLP), and convolutional neural network (CNN) approaches. The substantial number of recurrent iterations is the fundamental reason behind the limitations of RNNs in LTSF. To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: segment-wise iterations and parallel multistep forecasting (PMF). RNNs that combine these strategies, called SegRNN, significantly reduce the required recurrent iterations for LTSF, resulting in notable improvements in forecast accuracy and inference speed. Extensive experiments demonstrate that SegRNN not only outperforms state-of-the-art Transformer-based models but also reduces runtime and memory usage by more than 78%, making it highly suitable for resource-constrained IoT scenarios. These achievements provide strong evidence that RNNs continue to excel in LTSF tasks and encourage further exploration of this domain with more RNN-based approaches. The code is available at: https://github.com/lss-1138/SegRNN.