Summary of Sequential Order-robust Mamba For Time Series Forecasting, by Seunghan Lee et al.
Sequential Order-Robust Mamba for Time Series Forecasting
by Seunghan Lee, Juri Hong, Kibok Lee, Taeyoung Park
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 Mamba, a promising alternative to Transformers, has near-linear complexity in processing sequential data. However, recent studies adopted Mamba for capturing channel dependencies (CD) in time series (TS) data, introducing a sequential order bias. To address this issue, SOR-Mamba proposes two key strategies: regularization to minimize the discrepancy between two embedding vectors generated from data with reversed channel orders, and eliminating 1D-convolution to capture local information. Additionally, CCM (channel correlation modeling) pretraining preserves correlations between channels in the latent space, enhancing CD capture. Experimental results demonstrate the efficacy of SOR-Mamba across standard and transfer learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new way to process data that has some benefits over other methods called Transformers. However, when we use Mamba for certain types of data, it can be biased towards certain orders in the data. To fix this problem, scientists created SOR-Mamba, which includes two main ideas: one that helps reduce errors caused by different data orders and another that removes a type of processing step that was not needed. They also developed CCM, which helps preserve important relationships between different parts of the data. By doing these things, SOR-Mamba can be more effective at predicting future data. |
Keywords
» Artificial intelligence » Embedding » Latent space » Pretraining » Regularization » Time series » Transfer learning
