Li, Chunshien ; Chiang, Tai-Wei
Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.
Hybrid and Ensemble Methods in Machine Learning
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 well-known 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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 22, Number 4 (2012) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Oct 14, 2021
Sep 15, 2021
|Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence||Oct 14, 2021|
Sikora, Marek Sikora, Barbara Korbicz, Józef - red. Uciński, Dariusz - red.