Summary of Long Range Switching Time Series Prediction Via State Space Model, by Jiaming Zhang et al.
Long Range Switching Time Series Prediction via State Space Model
by Jiaming Zhang, Yang Ding, Yunfeng Gao
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study explores the combination of two models, Structured State Space Model (S4) and Switching Non-linear Dynamics System (SNLDS), to improve inference techniques for long-range dependencies in switching time series. The authors propose a new methodology that fuses S4 and SNLDS, leveraging their strengths to effectively segment and reproduce long-range dependencies in datasets such as the 1-D Lorenz dataset and the 2-D bouncing ball dataset. This approach is shown to outperform standalone SNLDS in these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two models to make predictions about patterns that change over time. It’s like trying to understand how a ball bounces – sometimes it goes high, sometimes it goes low. The authors developed a new way of using these models together to get better results. They tested this method on some tricky datasets and showed that it works really well. |
Keywords
» Artificial intelligence » Inference » Time series
