Summary of Deep Coupling Network For Multivariate Time Series Forecasting, by Kun Yi et al.
Deep Coupling Network For Multivariate Time Series Forecasting
by Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a novel deep learning framework for multivariate time series (MTS) forecasting, which simultaneously captures intra- and inter-series relationships among time series data. The authors argue that previous work has neglected multi-order interactions within and between time series data, leading to decreased forecasting accuracy. They develop a comprehensive relationship learning mechanism using mutual information and design a DeepCN model consisting of coupling, variable representation, and inference modules. Experimental results on seven real-world datasets show that the proposed approach outperforms state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting what will happen in multiple things happening at different times. Right now, we don’t do this very well because we’re not taking into account all the connections between these things. The authors want to fix this by creating a new way of understanding how these things are connected and using that to make better predictions. They call their new method DeepCN. It’s like a special computer program that looks at all the relationships between different things happening at different times and uses that to make predictions. This is important because we use this kind of prediction in many areas, such as weather forecasting or predicting how much money people will spend. |
Keywords
* Artificial intelligence * Deep learning * Inference * Time series
