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

Chunshien Li; Tai-Wei Chiang

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 4, page 787-800
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Chunshien Li, and Tai-Wei Chiang. "Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence." International Journal of Applied Mathematics and Computer Science 22.4 (2012): 787-800. <http://eudml.org/doc/244585>.

@article{ChunshienLi2012,
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.},
author = {Chunshien Li, Tai-Wei Chiang},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {complex fuzzy set; complex neuro-fuzzy system; hierarchical multi-swarm particle swarm optimization; recursive least squares estimator; time series forecasting},
language = {eng},
number = {4},
pages = {787-800},
title = {Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence},
url = {http://eudml.org/doc/244585},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Chunshien Li
AU - Tai-Wei Chiang
TI - Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 4
SP - 787
EP - 800
AB - 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.
LA - eng
KW - complex fuzzy set; complex neuro-fuzzy system; hierarchical multi-swarm particle swarm optimization; recursive least squares estimator; time series forecasting
UR - http://eudml.org/doc/244585
ER -

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