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Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence Cover

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

Open Access
|Dec 2012

Abstract

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the wellknown Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.

DOI: https://doi.org/10.2478/v10006-012-0058-x | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 787 - 800
Published on: Dec 28, 2012
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2012 Chunshien Li, Tai-Wei Chiang, published by Sciendo
This work is licensed under the Creative Commons License.