Volume 54, Number 2, March-April 2020
|Page(s)||597 - 614|
|Published online||05 March 2020|
Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization
Department of Mathematics, NIT Durgapur, Durgapur 713209, West Bengal, India
* Corresponding author: firstname.lastname@example.org
Accepted: 13 December 2019
In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.
Mathematics Subject Classification: 91B84 / 46A55 / 28E10 / 68W50
Key words: Time series forecasting / fuzzified Choquet integral / fuzzy measure / signed fuzzy measure / intuitionistic fuzzy measure / genetic algorithm
© EDP Sciences, ROADEF, SMAI 2020
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