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 Hybrid Model of Self-Organized Map and Integrated Fuzzy Rules with Support Vector Machine: Application to Stock Price Analysis
Tác giả hoặc Nhóm tác giả: Duc-Hien Nguyen, Van-Minh Le
Nơi đăng: Information Systems Design and Intelligent Applications; Số: 1;Từ->đến trang: 314-322;Năm: 2018
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Prediction of stock price is always an interesting task. However, it is not easy to make this prediction with high accuracy. Recently, plenty of combinations of statistical methods have been proposed. The main direction of these methods is that combination of regression learner (e.g., SVM) and a clustering of data (e.g., SOM). While these methods make relative success, their extensibility is still under discussion. In this paper, we propose an hybrid model of self-organized map and integrated fuzzy rules with support vector machine. The proposition method is evaluated to be a good approach to apply to stock price analysis. Moreover, this method provides interpretable rules which can be understood, calibrated, and modified by experts in order to direct the learning phase.
ABSTRACT
Prediction of stock price is always an interesting task. However, it is not easy to make this prediction with high accuracy. Recently, plenty of combinations of statistical methods have been proposed. The main direction of these methods is that combination of regression learner (e.g., SVM) and a clustering of data (e.g., SOM). While these methods make relative success, their extensibility is still under discussion. In this paper, we propose an hybrid model of self-organized map and integrated fuzzy rules with support vector machine. The proposition method is evaluated to be a good approach to apply to stock price analysis. Moreover, this method provides interpretable rules which can be understood, calibrated, and modified by experts in order to direct the learning phase.
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