Summary of Ar-sieve Bootstrap For the Random Forest and a Simulation-based Comparison with Rangerts Time Series Prediction, by Cabrel Teguemne Fokam and Carsten Jentsch and Michel Lang and Markus Pauly
AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction
by Cabrel Teguemne Fokam, Carsten Jentsch, Michel Lang, Markus Pauly
First submitted to arxiv on: 1 Oct 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 A novel combination of Random Forest (RF) and a residual bootstrapping technique is proposed for time series prediction. The new approach, which incorporates the Autoregressive-Sieve Bootstrap (ARSB), aims to better account for the Data Generating Process (DGP) while resampling observations. A simulation study using synthetic data from different DGPs demonstrates that RF with ARSB provides greater accuracy compared to other bootstrap strategies, but at some efficiency costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of scientists developed a new way to use a machine learning algorithm called Random Forest (RF) for predicting future values in a sequence of numbers. They combined RF with another technique that helps the algorithm understand how the data was generated. This combination worked better than other approaches, but it took longer to make predictions. |
Keywords
» Artificial intelligence » Autoregressive » Bootstrapping » Machine learning » Random forest » Synthetic data » Time series
