Summary of Lino: Advancing Recursive Residual Decomposition Of Linear and Nonlinear Patterns For Robust Time Series Forecasting, by Guoqi Yu et al.
LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting
by Guoqi Yu, Yaoming Li, Xiaoyu Guo, Dayu Wang, Zirui Liu, Shujun Wang, Tong Yang
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed LiNo model, which combines recursive residual decomposition with explicit extraction of linear and nonlinear patterns, achieves state-of-the-art performance on thirteen real-world benchmarks for univariate and multivariate forecasting scenarios. The LiNo framework utilizes a Li block for linear pattern extraction and a No block for nonlinear pattern modeling, allowing for alternative and recursive pattern extraction. This advanced approach delivers more robust and precise results compared to current forecasting models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiNo is a new way of predicting future events by breaking down complex patterns into simpler ones. It’s like taking apart a big puzzle to find the individual pieces that make it up. LiNo does this by finding both straight-line patterns (like trends) and curvy patterns (like seasonality). It then uses these patterns to predict what will happen next. This method is really good at making predictions and can even handle complex situations where multiple patterns are mixed together. |
