  Long time series forecasting aims to utilize historical information to
forecast future states over extended horizons. Traditional RNN-based series
forecasting methods struggle to effectively address long-term dependencies and
gradient issues in long time series problems. Recently, SegRNN has emerged as a
leading RNN-based model tailored for long-term series forecasting,
demonstrating state-of-the-art performance while maintaining a streamlined
architecture through innovative segmentation and parallel decoding techniques.
Nevertheless, SegRNN has several limitations: its fixed segmentation disrupts
data continuity and fails to effectively leverage information across different
segments, the segmentation strategy employed by SegRNN does not fundamentally
address the issue of information loss within the recurrent structure. To
address these issues, we propose the ISMRNN method with three key enhancements:
we introduce an implicit segmentation structure to decompose the time series
and map it to segmented hidden states, resulting in denser information exchange
during the segmentation phase. Additionally, we incorporate residual structures
in the encoding layer to mitigate information loss within the recurrent
structure. To extract information more effectively, we further integrate the
Mamba architecture to enhance time series information extraction. Experiments
on several real-world long time series forecasting datasets demonstrate that
our model surpasses the performance of current state-of-the-art models.
