Summary of Nbmlss: Probabilistic Forecasting Of Electricity Prices Via Neural Basis Models For Location Scale and Shape, by Alessandro Brusaferri and Danial Ramin and Andrea Ballarino
NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape
by Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
First submitted to arxiv on: 21 Nov 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 This paper presents a novel approach for interpretable multi-horizon forecasting using flexible neural networks in distributional regression settings. The Neural Basis Model for Location, Scale and Shape combines principles from GAMLSS and shared basis decomposition with linear projections to support stepwise feature shape functions. Experiments on multiple market regions demonstrate comparable probabilistic forecasting performance to distributional neural networks while providing insights into model behavior through learned nonlinear feature maps. This work enables forecasters to gain detailed insights into the underlying mechanisms driving predicted feature-conditioned distribution parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machine learning models predict future events. It introduces a new way of doing this called Neural Basis Model for Location, Scale and Shape. This method allows us to see how the model works and what it’s doing when it predicts something. The researchers tested this approach on different regions and found that it performed just as well as other methods while giving us more information about how the model made its predictions. |
Keywords
* Artificial intelligence * Machine learning * Regression
