Summary of Psformer: Parameter-efficient Transformer with Segment Attention For Time Series Forecasting, by Yanlong Wang et al.
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
by Yanlong Wang, Jian Xu, Fei Ma, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang
First submitted to arxiv on: 3 Nov 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 This paper introduces a novel transformer architecture for time series forecasting, addressing the challenges of high-dimensional data and long-term dependencies. The PSformer model combines two key innovations: parameter sharing (PS) to reduce training parameters, and Spatial-Temporal Segment Attention (SegAtt) to capture local spatio-temporal dependencies. This approach improves model efficiency, scalability, and forecasting performance, outperforming popular baselines and other transformer-based approaches on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict what will happen in the future based on data from the past. It’s like trying to guess what will happen tomorrow or next week based on what happened yesterday or last month. The method uses special computer tools called transformers and two new ideas: sharing parameters between different parts of the data, and looking at different segments or chunks of the data to find patterns. This helps make the predictions more accurate and efficient. The researchers tested this method on some real-world datasets and it worked better than other methods that people have tried before. |
Keywords
» Artificial intelligence » Attention » Time series » Transformer
