Summary of Large Width Penalization For Neural Network-based Prediction Interval Estimation, by Worachit Amnuaypongsa et al.
Large width penalization for neural network-based prediction interval estimation
by Worachit Amnuaypongsa, Jitkomut Songsiri
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 probabilistic forecasting approach is developed to quantify uncertainty in highly uncertain environments. The prediction interval (PI) is used to show upper and lower bounds of predictions associated with a confidence level, allowing for more accurate risk management. A PI loss function is proposed that penalizes large PI widths, which can lead to significant cost savings by reducing over-allocation of reserve resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forecasting accuracy in uncertain environments is challenging because systems are stochastic. Deterministic forecasting only provides point estimates and can’t capture potential outcomes. Probabilistic forecasting helps quantify uncertainty, with PIs showing upper and lower bounds of predictions associated with a confidence level. A narrower PI width reduces costs for backup resources. |
Keywords
» Artificial intelligence » Loss function
