Summary of Surrogate Modeling For Explainable Predictive Time Series Corrections, by Alfredo Lopez et al.
Surrogate Modeling for Explainable Predictive Time Series Corrections
by Alfredo Lopez, Florian Sobieczky
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 local surrogate approach offers an innovative solution for explainable time-series forecasting, building upon an initially non-interpretable predictive model that refines a classical base model’s forecast. By subtracting the error prediction from the original data, the difference in model parameters is obtained, providing valuable insights into underlying patterns in the data. This method showcases its potential to uncover and interpret complex relationships within time-series data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a machine that predicts what will happen next in a sequence of numbers. But sometimes, this prediction isn’t entirely accurate. The paper proposes a way to understand why these predictions are off. It uses an initial model, which is not easy to understand, and refines it by removing the errors. By doing so, the method reveals how the underlying patterns in the data contribute to the inaccuracies. This helps us better understand complex sequences of numbers. |
Keywords
» Artificial intelligence » Time series
