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Multilayer stock forecasting model using fuzzy time series.

Javedani Sadaei H, Lee MH - ScientificWorldJournal (2014)

Bottom Line: In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding.In this study, we propose a multilayer model for stock market forecasting including five logical significant layers.Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

ABSTRACT
After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

Show MeSH
Proposed multilayer model.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: Proposed multilayer model.

Mentions: Having discussed the key points in the introduction section, we proposed a multilayer model that could be beneficial for stock market forecasting by using FTS methods. The proposed model contains five logical meaningful layers as displayed in Figure 1.


Multilayer stock forecasting model using fuzzy time series.

Javedani Sadaei H, Lee MH - ScientificWorldJournal (2014)

Proposed multilayer model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3926375&req=5

fig1: Proposed multilayer model.
Mentions: Having discussed the key points in the introduction section, we proposed a multilayer model that could be beneficial for stock market forecasting by using FTS methods. The proposed model contains five logical meaningful layers as displayed in Figure 1.

Bottom Line: In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding.In this study, we propose a multilayer model for stock market forecasting including five logical significant layers.Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

ABSTRACT
After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

Show MeSH