Summary of Vcformer: Variable Correlation Transformer with Inherent Lagged Correlation For Multivariate Time Series Forecasting, by Yingnan Yang et al.
VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting
by Yingnan Yang, Qingling Zhu, Jianyong Chen
First submitted to arxiv on: 19 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 The proposed Variable Correlation Transformer (VCformer) addresses the limitations of current multivariate time series (MTS) forecasting methods by utilizing a Variable Correlation Attention (VCA) module to capture intricate cross-correlations between variables. The VCA module calculates and integrates cross-correlation scores corresponding to different lags, enabling the extraction of multivariate relationships. Additionally, the Koopman Temporal Detector (KTD) is developed to address non-stationarity in time series. Experiments on eight real-world datasets demonstrate VCformer’s effectiveness, achieving top-tier performance compared to state-of-the-art baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach for forecasting multivariate time series data, which is important because it can help us make better predictions about things like weather and energy usage. The method uses two key components: one that helps find relationships between different variables, and another that helps deal with changes over time in the data. This method is tested on many real-world datasets and performs well compared to other methods. |
Keywords
» Artificial intelligence » Attention » Time series » Transformer
