Summary of Online Distributional Regression, by Simon Hirsch et al.
Online Distributional Regression
by Simon Hirsch, Jonathan Berrisch, Florian Ziel
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME)
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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 methodology for online estimation of regularized, linear distributional models is designed to tackle large-scale streaming data common in modern machine learning applications. This is particularly relevant in fields like supply chain management, weather and meteorology, energy markets, and finance that rely on probabilistic forecasts. The algorithm combines recent developments in LASSO model estimation with the GAMLSS framework. In a case study on day-ahead electricity price forecasting, the incremental estimation approach demonstrates competitive performance while reducing computational effort. The algorithms are implemented in a computationally efficient Python package. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to learn from big data that is important for many fields like predicting weather and energy prices. It combines two existing methods to create an algorithm that can learn from data as it comes in, which is useful for making quick predictions. The algorithm was tested on forecasting electricity prices one day ahead, and it did well while using less computer power than other approaches. |
Keywords
» Artificial intelligence » Machine learning
