Summary of Deep Time Series Models: a Comprehensive Survey and Benchmark, by Yuxuan Wang et al.
Deep Time Series Models: A Comprehensive Survey and Benchmark
by Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong Liu, Mingsheng Long, Jianmin Wang
First submitted to arxiv on: 18 Jul 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 paper presents a comprehensive review of deep learning models for time series analysis, exploring the design of basic modules and model architectures. The authors also develop and release the Time Series Library (TSLib), a benchmarking tool that includes 24 mainstream models, 30 datasets from various domains, and five prevalent analysis tasks. Evaluating 12 advanced deep time series models on different tasks using TSLib reveals that specific structures are well-suited for distinct analytical tasks. This research provides insights for the adoption of deep time series models in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series data is all around us! It’s a sequence of numbers, like temperatures or stock prices, arranged in a special order. Time series can be tricky to analyze because they’re complex and change over time. Scientists have been studying them for centuries! Recently, new deep learning models have come along that are super good at analyzing time series data. This paper looks at different types of deep learning models used for time series analysis and makes a special library called TSLib that has lots of examples and tests to help people compare how well these models work. |
Keywords
» Artificial intelligence » Deep learning » Time series
