NDLI: Fuzzy models for predicting time series stock price index

Content Provider	Springer Nature Link
Author	Hwang, Heesoo Oh, Jinsung
Copyright Year	2010
Abstract	Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy models have recently been used to predict stock market prices because they are capable of extracting useful information from large sets of data without any assumption about a mathematical model. In this paper, three types of fuzzy rule formats to predict daily and weekly stock price indexes were presented. Their premises and consequences were composed of trapezoidal membership functions and novel nonlinear equations, respectively. As the most effective indicators for stock prediction, the information used in traditional candle stick-chart analysis was newly employed as input variables of our fuzzy models. The optimal fuzzy models were identified through an evolutionary process of differential evolution (DE). The different types of fuzzy models to predict the daily and weekly open, high, low, and close prices of the Korea Composite Stock Price Index (KOSPI) were built, and their performances were compared.
Starting Page	702
Ending Page	706
Page Count	5
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	8
Issue Number	3
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2010-06-05
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Differential evolution fuzzy model stock prediction time series prediction Control Robotics Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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