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 towards Transformer, MLP, and CNN approaches. The substantial number of recurrent iterations are the fundamental reasons 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 Multi-step Forecasting (PMF). RNNs that combine these strategies, namely 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 SOTA Transformer-based models but also reduces runtime and memory usage by more than 78%. 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 source code is coming soon.

翻译：基于RNN的方法在处理过长的回溯窗口和预测范围时，在长期时间序列预测（LTSF）领域面临挑战。因此，该领域的主导地位已转向Transformer、MLP和CNN方法。大量的递归迭代是RNN在LTSF中受到限制的根本原因。为解决这些问题，我们提出了两种新颖策略来减少RNN在LTSF任务中的迭代次数：分段迭代和平行多步预测（PMF）。结合这些策略的RNN（即SegRNN）显著减少了LTSF所需的递归迭代次数，从而在预测精度和推理速度上取得了显著改进。广泛实验表明，SegRNN不仅优于基于Transformer的SOTA模型，还将运行时间和内存使用量减少了78%以上。这些成果有力证明了RNN在LTSF任务中仍能表现出色，并鼓励使用更多基于RNN的方法进一步探索该领域。源代码即将发布。