Summary of Enhancing Transformer-based Models For Long Sequence Time Series Forecasting Via Structured Matrix, by Zhicheng Zhang et al.
Enhancing Transformer-based models for Long Sequence Time Series Forecasting via Structured Matrix
by Zhicheng Zhang, Yong Wang, Shaoqi Tan, Bowei Xia, Yujie Luo
First submitted to arxiv on: 21 May 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 In this research paper, the authors propose a novel architectural framework to enhance Transformer-based models for long sequence time series forecasting. The framework integrates Surrogate Attention Blocks (SAB) and Surrogate Feed-Forward Neural Network Blocks (SFB) to reduce both time and space complexity while maintaining the expressive power of the original model. This approach is demonstrated to achieve an average performance improvement of 12.4% across five distinct time series tasks, with a significant reduction in parameter counts of 61.3%. The authors claim that this framework can improve the efficiency of self-attention mechanisms in Transformer-based models for long sequence forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make Transformer-based models better at predicting long sequences of data. These models are good at finding patterns, but they use a lot of computer power and memory. The authors suggest changing how the model works by using “surrogate” blocks instead of some of the original blocks. This makes the model faster and more efficient while still keeping its ability to find patterns. They tested this new approach on 10 different models and found that it worked better in most cases. |
Keywords
» Artificial intelligence » Attention » Neural network » Self attention » Time series » Transformer
