Abstract
Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great advantages such as potential scientific discovery of new laws, as well as explainability, feature engineering as well as fast inference. Similarly, deep learning based techniques has shown an extraordinary ability of modeling complex patterns. The present paper aims at applying a recent end-to-end symbolic regression technique, i.e. the equation learner (EQL), to get an analytical equation for wind speed forecasting. We show that it is possible to derive an analytical equation that can achieve reasonable accuracy for short term horizons predictions only using few number of features.
| Original language | English |
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| Title of host publication | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings |
| Publisher | IEEE |
| ISBN (Electronic) | 9781728190488 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States Duration: 5 Dec 2021 → 7 Dec 2021 |
Conference
| Conference | 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 |
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| Country/Territory | United States |
| City | Orlando |
| Period | 5/12/21 → 7/12/21 |
Bibliographical note
Funding Information:All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. John Zervos and Tyler Prentiss report grants from the United Way of Southeastern Michigan, Vattikuti Foundation, and Abbott Laboratories during the conduct of the study. Marcus J. Zervos reports grants from Pfizer, Merck, and Serono, outside the submitted work. No other potential conflicts of interest were disclosed.
Publisher Copyright:
© 2021 IEEE.
Funding
All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. John Zervos and Tyler Prentiss report grants from the United Way of Southeastern Michigan, Vattikuti Foundation, and Abbott Laboratories during the conduct of the study. Marcus J. Zervos reports grants from Pfizer, Merck, and Serono, outside the submitted work. No other potential conflicts of interest were disclosed.
Keywords
- Deep learning
- Explainability
- Symbolic regression
- Weather data