Summary of Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-series Dependencies and Intra-series Variations Modeling, by Guoqi Yu et al.
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
by Guoqi Yu, Jing Zou, Xiaowei Hu, Angelica I. Aviles-Rivero, Jing Qin, Shujun Wang
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper, researchers introduce a novel approach for predicting multivariate time series data by combining learnable decomposition and dual attention mechanisms. The proposed method, called Leddam (LEarnable Decomposition and Dual Attention Module), is designed to capture dynamic trend information and inter-series dependencies more effectively than existing methods. This is achieved through the use of channel-wise self-attention and autoregressive self-attention. To evaluate the effectiveness of Leddam, experiments were conducted across eight open-source datasets and compared with state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to predict multiple time series by breaking down each series into simpler parts and focusing on patterns between them. This helps improve forecasting accuracy. The authors test their method on many real-world datasets and show it works better than other approaches. |
Keywords
* Artificial intelligence * Attention * Autoregressive * Self attention * Time series
