ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecasting

TL;DR

ISMRNN introduces an implicit segmentation approach combined with residual encoding and Mamba architecture to improve long-term time series forecasting, outperforming existing models in real-world datasets.

Contribution

The paper proposes a novel implicit segmentation method with residual encoding and Mamba integration to enhance long-term series forecasting accuracy.

Findings

Outperforms state-of-the-art models on multiple datasets

Effectively captures long-term dependencies in time series

Reduces information loss during segmentation

Abstract

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.