Summary of Transparent Networks For Multivariate Time Series, by Minkyu Kim et al.
Transparent Networks for Multivariate Time Series
by Minkyu Kim, Suan Lee, Jinho Kim
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 A novel transparent neural network model, Generalized Additive Time Series Model (GATSM), is proposed for time series prediction. This model consists of two parts: independent feature networks to learn feature representations and a transparent temporal module to capture temporal patterns across different time steps using these features. GATSM effectively handles dynamic-length time series while preserving transparency. Empirical experiments show that it outperforms existing generalized additive models and achieves comparable performance to black-box models like recurrent neural networks and Transformer. GATSM also finds interesting patterns in time series. The model is evaluated on various benchmarks, including real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of machine learning model called GATSM can help us better understand what’s happening with time series data. Time series data is like a list of numbers that shows how something changes over time. Right now, there aren’t many models that are good at understanding this type of data and also being easy to interpret. GATSM is different because it can handle long lists of numbers and find patterns in the data. It’s also really good at making predictions about what will happen next. The people who made GATSM tested it on some real-world datasets and found that it works well. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Time series » Transformer
