Stock market forecasting by using a hybrid model of exponential fuzzy time series
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- 作者：Fatemeh Mirzaei Talarposhtia ; mirzaei.fat@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Hossein Javedani Sadaeib ; jshossein@ufmg.br" class="auth_mail" title="E-mail the corresponding author ; Rasul Enayatifarb ; r.enayatifar@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Frederico Gadelha Guimarã ; esc ; fredericoguimaraes@ufmg.br" class="auth_mail" title="E-mail the corresponding author ; Maqsood Mahmudd ; maqsood@mcst.edu.sa" class="auth_mail" title="E-mail the corresponding author ; Tayyebeh Eslamie ; tayeb.eslami@gmail.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Fuzzy time series ; Exponential fuzzy time series ; Stock forecasting ; Learning automata particle swarm optimization
- 刊名：International Journal of Approximate Reasoning
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：70
- 期：Complete
- 页码：79-98
- 全文大小：684 K
文摘
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In this study a two phase approach is proposed based on exponential fuzzy time series and learning automata.
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In the first phase, the optimal lengths of intervals are estimated by applying LA based EAs in training set.
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Second phase aim is to estimate certain adjusting parameters for minimizing errors in training set.
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The conventional FTS in the first phase is applied and in the second phase EFTS is employed.
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Forty six case studies from five stock index databases are employed in extensive experiments.