Summary of Probabilistic Reduced-dimensional Vector Autoregressive Modeling with Oblique Projections, by Yanfang Mo and S. Joe Qin
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling with Oblique Projections
by Yanfang Mo, S. Joe Qin
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 presents a novel probabilistic reduced-dimensional vector autoregressive (PredVAR) model to extract low-dimensional dynamics from high-dimensional noisy data. The PredVAR model partitions the measurement space into two subspaces: one for reduced-dimensional dynamics and another for static components. An optimal oblique decomposition is derived for best predictability, followed by an iterative algorithm using maximum likelihood and expectation-maximization (EM). This approach yields dynamic latent variables with rank-ordered predictability and a consistent latent VAR model. The proposed method outperforms existing approaches in terms of performance and efficiency, as demonstrated by experiments on synthetic Lorenz data and real-world industrial process data from Eastman Chemical. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding patterns in noisy data. It’s like trying to find the melody in a song with lots of background noise. The researchers developed a new way to do this called PredVAR. They divided the noise into two parts: one that changes over time and one that stays the same. Then, they used an algorithm to update these parts until they got the best possible result. This approach worked better than other methods in tests with fake data and real-world industrial data. |
Keywords
* Artificial intelligence * Autoregressive * Likelihood
