Summary of Lseattention Is All You Need For Time Series Forecasting, by Dizhen Liang
LSEAttention is All You Need for Time Series Forecasting
by Dizhen Liang
First submitted to arxiv on: 31 Oct 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 Transformer-based architectures have achieved remarkable success in natural language processing and computer vision, but their performance in multivariate long-term forecasting often falls short compared to simpler linear baselines. To address this gap, we introduce LATST, a novel approach designed to mitigate entropy collapse and training instability common challenges in Transformer-based time series forecasting. We rigorously evaluate LATST across multiple real-world multivariate time series datasets, demonstrating its ability to outperform existing state-of-the-art Transformer models. Notably, LATST manages to achieve competitive performance with fewer parameters than some linear models on certain datasets, highlighting its efficiency and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve the performance of computer programs that forecast the future based on past data. These programs are called Transformers, and they’re great at tasks like language translation and image recognition. However, when it comes to forecasting multiple related values over time (like stock prices or weather patterns), they often don’t do as well as simpler methods. The new approach, called LATST, is designed to fix this problem by making the program more stable and efficient. The researchers tested LATST on several real-world datasets and found that it performs better than other similar programs while also using fewer resources. |
Keywords
» Artificial intelligence » Natural language processing » Time series » Transformer » Translation
