Summary of Ismrnn: An Implicitly Segmented Rnn Method with Mamba For Long-term Time Series Forecasting, by Gaoxiang Zhao and Li Zhou and Xiaoqiang Wang
ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecasting
by GaoXiang Zhao, Li Zhou, XiaoQiang Wang
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Long-term time series forecasting is a challenging task that requires effectively addressing dependencies and gradient issues in historical data to predict future states over extended horizons. Recently, SegRNN has emerged as a leading RNN-based model for long-term series forecasting, offering state-of-the-art performance while maintaining a streamlined architecture through innovative segmentation and parallel decoding techniques. However, SegRNN has limitations: its fixed segmentation disrupts data continuity and fails to effectively leverage information across different segments. To address these issues, the proposed ISMRNN method introduces an implicit segmentation structure to decompose time series, map it to segmented hidden states, and enhance information exchange during the segmentation phase. Additionally, residual structures are incorporated in the encoding layer to mitigate information loss within the recurrent structure. The Mamba architecture is also integrated to extract time series information more effectively. Experimental results on real-world long time series forecasting datasets demonstrate that ISMRNN surpasses state-of-the-art model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Forecasting what will happen in the future by looking at past data is a difficult task. Historically, models like SegRNN have done well at this job but had some limitations. They didn’t work well with long periods of time and couldn’t use all the information they found. To fix these problems, researchers created a new model called ISMRNN. This model breaks down the past data into smaller chunks and uses each chunk to help predict what will happen next. It also remembers things it learned from earlier in the process to make better predictions later on. The results show that this new model does even better than SegRNN at predicting what will happen in the future. |
Keywords
* Artificial intelligence * Rnn * Time series
