A sequential data mining method for modelling solar magnetic cycles

MWITONDI, Kassim S., SAID, Raeed T. and YOUSIF, Adil E. (2012). A sequential data mining method for modelling solar magnetic cycles. In: HUANG, Tingwen, ZENG, Zingang, LI, Chuandong and LEUNG, Chi Sing, (eds.) Neural information processing : 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part I. Lecture Notes in Computer Science (7663). Springer, 296-304.

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Abstract

We propose an adaptive data-driven approach to modelling solar magnetic activity cyclesbased on a sequential link between unsupervised and supervised modelling. Monthly sunspot numbers spanning over hundreds of years – from the mid-18th century to the first quarter of 2012 - obtained from the Royal Greenwich Observatory provide a reliable source of training and validation sets.An indicator variable is used to generate class labels and internal parameters which are used to separate high from low activity cycles. Our results show that by maximising data-dependent parameters and using them as inputs to a support vector machine model we obtain comparatively more robust and reliable predictions. Finally, we demonstrate how the method can be adapted to other unsupervised and supervised modelling applications. Keywords : Data clustering – Data mining – Predictive modelling – Solar magnetic activity – Sunspots – Supervised modelling – Support Vector Machines – Unsupervised Modelling

Item Type:	Book Section
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1007/978-3-642-34475-6_36
Page Range:	296-304
Depositing User:	Kassim Mwitondi
Date Deposited:	24 Sep 2012 16:19
Last Modified:	18 Mar 2021 22:30
URI:	https://shura.shu.ac.uk/id/eprint/5266

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