Summary of Recent Trends in Modelling the Continuous Time Series Using Deep Learning: a Survey, by Mansura Habiba et al.
Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
by Mansura Habiba, Barak A. Pearlmutter, Mehrdad Maleki
First submitted to arxiv on: 13 Sep 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 This paper explores the challenges of modeling continuous-time series data, which is essential for various modern applications such as healthcare, finance, and IoT. Despite the importance of processing massive amounts of time-series data, existing deep learning models face limitations due to diversity in attributes, behavior, duration, energy, and data sampling rates. The authors review recent developments in deep learning models and their contributions to solving difficulties in modeling continuous-time series data. They identify limitations of existing neural network models and open issues, highlighting the need for a comprehensive understanding of trends and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sense of really long sequences of data that change over time, which is important for things like predicting stock prices or monitoring patient health. Right now, it’s hard to use computers to analyze this kind of data because the patterns can be too complex. The researchers are trying to figure out what’s going wrong and how to make better models to handle these kinds of problems. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Time series
