Gu, Y., Wei, H., Balikhin, M. et al. (2 more authors) (2019) Machine learning enhanced NARMAX model for Dst index forecasting. In: Proceedings of the 25th International Conference on Automation and Computing (ICAC). 25th International Conference on Automation and Computing (ICAC' 19), 05-07 Sep 2019, Lancaster, UK. IEEE ISBN 9781728125183
Abstract
As many systems and equipment are sensitive to magnetic disturbances, it is important to understand the magnetosphere system, to reduce the negative effect caused by severe space weather situations. The disturbance storm time (Dst) index is used to measure the magnetic disturbances and it is correlated with solar wind variables. This study presents a new machine learning enhanced NARMAX (MLE- NARMAX) model for 3 hours ahead forecasting of Dst index. An important advantage of the MLE-NARMAX model is that it provides a transparent and explainable model structure. The model performance is tested over three typical strong storm periods, where the prediction skills are 0.9734, 0.9598 and 0.9206 in terms of correlation, and 0.9474, 0.9173, and 0.8333 in terms prediction efficiency (PE). Compared to the conventional NARX model, the MLE-NARMAX produces better model predictions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Magnetic Disturbance; Dst Index; NARMAX model; Machine Learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council (EPSRC) EP/I011056/1 and EP/H00453X/1 EU Horizon 2020 637302 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jul 2019 09:10 |
Last Modified: | 11 Nov 2020 01:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/IConAC.2019.8895027 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148522 |