Summary of Revisiting Attention For Multivariate Time Series Forecasting, by Haixiang Wu
Revisiting Attention for Multivariate Time Series Forecasting
by Haixiang Wu
First submitted to arxiv on: 18 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 This paper proposes two novel attention mechanisms for Multivariate Time-Series Forecasting (MTSF): Frequency Spectrum attention (FSatten) and Scaled Orthogonal attention (SOatten). FSatten uses the Fourier transform to embed sequences in a frequency domain space, introducing Multi-head Spectrum Scaling (MSS) to replace the conventional linear mapping of Q and K. This approach outperforms the conventional attention without changing mainstream architectures, accurately capturing periodic dependencies between sequences. SOatten employs orthogonal embedding and Head-Coupling Convolution (HCC) based on neighboring similarity bias to learn comprehensive dependency patterns. Experimental results show that both FSatten and SOatten surpass the state-of-the-art using conventional attention, making them viable alternatives as basic attention mechanisms for MTSF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops two new attention methods for predicting future values in time series data: Frequency Spectrum attention (FSatten) and Scaled Orthogonal attention (SOatten). The first method uses a special kind of math called Fourier transform to understand patterns in the data. The second method is like a puzzle that helps the model learn how different pieces fit together. Both methods work better than previous methods, making them useful for predicting things like stock prices or weather. |
Keywords
* Artificial intelligence * Attention * Embedding * Time series
