Summary of Modeling Latent Non-linear Dynamical System Over Time Series, by Ren Fujiwara et al.
Modeling Latent Non-Linear Dynamical System over Time Series
by Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 abstract discusses a novel approach to modeling non-linear dynamical systems using time series data. The authors propose Latent Non-Linear equation modeling (LaNoLem), which includes an alternating minimization algorithm for estimating latent states and model parameters. LaNoLem can effectively model latent non-linear dynamics and solve circular dependencies caused by the presence of latent states. The method achieves competitive performance in estimating dynamics and outperforms other methods in prediction, with criteria to control model complexity without human intervention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand complex systems using time series data. It’s like trying to figure out how a car moves based on the sound of its engine. The researchers developed a special method called LaNoLem that can learn about these systems and make predictions about what will happen next. This is important because it could help us better understand things like weather patterns or stock prices. |
Keywords
» Artificial intelligence » Time series
