Summary of Ve: Modeling Multivariate Time Series Correlation with Variate Embedding, by Shangjiong Wang et al.
VE: Modeling Multivariate Time Series Correlation with Variate Embedding
by Shangjiong Wang, Zhihong Man, Zhenwei Cao, Jinchuan Zheng, Zhikang Ge
First submitted to arxiv on: 10 Sep 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 The proposed variate embedding (VE) pipeline aims to enhance multivariate time series forecasting by capturing correlations among variates. Current models, including those with a channel-independent final projection layer, struggle to model these dependencies. The VE pipeline combines Mixture of Experts and Low-Rank Adaptation techniques with unique embeddings for each variate, improving forecasting performance while controlling parameter size. This approach can be integrated into existing models to enhance multivariate forecasting capabilities. Experiments on four widely-used datasets demonstrate the effectiveness of the VE pipeline in grouping variates with similar temporal patterns and separating those with low correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to improve predicting future values based on past data from multiple variables. Currently, most methods have trouble understanding how these variables are connected. The authors developed a method called Variate Embedding (VE) that helps solve this problem. VE creates special representations for each variable and combines them with other techniques to make better predictions. This can be used in any model that predicts future values based on past data from multiple variables. The authors tested their method on four common datasets and showed it works well. |
Keywords
» Artificial intelligence » Embedding » Low rank adaptation » Mixture of experts » Time series
