Summary of Time Series Clustering with General State Space Models Via Stochastic Variational Inference, by Ryoichi Ishizuka et al.
Time Series Clustering with General State Space Models via Stochastic Variational Inference
by Ryoichi Ishizuka, Takashi Imai, Kaoru Kawamoto
First submitted to arxiv on: 29 Jun 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 A novel method for model-based time series clustering with mixtures of general state space models (MSSMs) is proposed, enabling the use of time series models specific to each cluster. This approach improves accuracy, enhances interpretability, and estimates latent variables using neural networks with normalizing flows as a variational estimator. The number of clusters can be estimated via Bayesian information criterion. To prevent local optima convergence, optimization tricks like entropy annealing are introduced. Experiments on simulated datasets demonstrate the effectiveness of this method for clustering, parameter estimation, and estimating cluster numbers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series data is used to group similar patterns together, but current methods don’t work well with non-linear or non-Gaussian data. This new approach fixes that problem by using special types of models called mixtures of general state space models. It’s like a puzzle where each piece has its own rules and the computer figures out how many pieces there are and what they mean. The method uses artificial intelligence to make it work, and tests show it does a great job at grouping data and understanding what it means. |
Keywords
» Artificial intelligence » Clustering » Optimization » Time series
