SegRNN: Segment Recurrent Neural Network for Long-Term Time Series
  Forecasting
Shengsheng Lin Weiwei Lin Wentai Wu Feiyu Zhao Ruichao Mo Haotong Zhang
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.