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.

Full text not available from this repository.
Link to published version:: 10.1007/978-3-642-34475-6_36

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: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number: 10.1007/978-3-642-34475-6_36
Related URLs:
Depositing User: Kassim Mwitondi
Date Deposited: 24 Sep 2012 16:19
Last Modified: 09 Nov 2016 16:40
URI: http://shura.shu.ac.uk/id/eprint/5266

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics