Ekpenyong, Francis, Samakovitis, Georgios ORCID: https://orcid.org/0000-0002-0076-8082, Kapetanakis, Stylianos and Petridis, Miltos (2021) Case retrieval with clustering for a case-based reasoning and inverse problem methodology: an investigation of financial bubbles. In: Meng, Hongying, Lei, Tao, Li, Maozhen, Li, Kenli, Xiong, Ning and Wang, Lipo, (eds.) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 88), 88 . Springer Link, Cham and New York, pp. 1515-1524. ISBN 978-3030706647 ; 978-3030706654 (doi:10.1007/978-3-030-70665-4_164)

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Abstract

This paper proposes an approach for predicting abnormal asset performance in traded securities, often referred to as ‘financial bubbles’. It uses an ensemble technique based on Case-based Reasoning (CBR) and Inverse Problems (IP), which we term IPCBR. More specifically we propose a Machine Learning formative strategy to determine the causes of stock behaviour, rather than to predict future time series points in fuzzy environments. In so doing, our paper contributes to more robust strategies in investigating financial bubbles. The framework uses a geometric pattern description of historical time series and applies clustering techniques to derive a model that generalizes those patterns onto observations. The model constitutes the forward approach to the IPCBR framework; our results demonstrate that, given the target problem, our CBR model provides a computationally inexpensive description of abnormal asset performance.

Item Type:	Book Section
Additional Information:	Discusses recent advances in natural computation, fuzzy systems, and knowledge discovery. Presents the proceedings of the 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020), held in Xi’an, China, from 1 to 3 August 2020. LNDECT, volume 88.
Uncontrolled Keywords:	case-based reasoning; forward problems; clustering; inverse problems; financial bubbles, Artificial intelligence
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HC Economic History and Conditions Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Publisher Publisher
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/30904

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