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A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting

Sadaei, H. J. and Enayatifar, R. and Lee, M. H. and Mahmud, M. (2016) A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Applied Soft Computing Journal, 40 . pp. 132-149. ISSN 1568-4946

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

In this study, a new kind of fuzzy set in fuzzy time series' field is introduced. It works as a trend estimator to be appropriate for fuzzy time series forecasting by reconnoitering trend of data appropriately. First, the historical data are fuzzified into differential fuzzy sets, and then differential fuzzy relationships are calculated. Second, differential fuzzy logic groups are established by grouping differential fuzzy relationships. Finally, in the defuzzification step, the forecasts are calculated. However, for increasing the accuracy of the models, an evolutionary algorithm, namely imperialist competitive algorithm is injected, to train the model. A massive stock data from four main stock databases have been selected for model validation. The final project, has shown that outperformed its counterparts in term of accuracy.

Item Type:Article
Uncontrolled Keywords:Algorithms, Evolutionary algorithms, Financial markets, Forecasting, Fuzzy logic, Fuzzy sets, Optimization, Time series, Defuzzifications, Fuzzy logic groups, Fuzzy relationship, Fuzzy time series, Imperialist competitive algorithms, Stock forecasting, Stock market forecasting, Trend, Electronic trading
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:73853
Deposited By: Haliza Zainal
Deposited On:20 Nov 2017 02:11
Last Modified:20 Nov 2017 02:11

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