Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

DOI

10.2991/ifsa-eusflat-15.2015.89How to use a DOI?

Keywords

Stock index prediction, mamdani-type fuzzy model, mutual information, Sequential forward input selection, cooperative random learning particle swarm optimization (CRPSO).

Abstract

In this paper, the integration of mutual information (MI) and fuzzy model is proposed to predict stock indexes with complex and non-linear characteristics. Technical indicators are considered as initial input candidates and significant inputs are determined by MI-based input selection method. To identify the structures and parameters of fuzzy models simultaneously, cooperative random learning particle swarm optimization (CRPSO), proposed by Zhao et al., is used. To confirm the effectiveness, the proposed method and comparison methods are applied to the Korea Composite Stock Price Index (KOSPI). The experimental results show that the proposed method, on average, outperforms other comparison methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jungwon Yu
AU  - Sungshin Kim
PY  - 2015/06
DA  - 2015/06
TI  - Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 624
EP  - 631
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.89
DO  - 10.2991/ifsa-eusflat-15.2015.89
ID  - Yu2015/06
ER  -