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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.

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Unprocessed data for year 2002 of DJI.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3926375&req=5

fig2: Unprocessed data for year 2002 of DJI.

Mentions: Hence, our proposed data preprocessing gives us new time series, that is, {ROI(t)} in new domain with less volatility and noisy effects. Notice and compare Figures 2 and 3.


Multilayer stock forecasting model using fuzzy time series.

Javedani Sadaei H, Lee MH - ScientificWorldJournal (2014)

Unprocessed data for year 2002 of DJI.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Unprocessed data for year 2002 of DJI.
Mentions: Hence, our proposed data preprocessing gives us new time series, that is, {ROI(t)} in new domain with less volatility and noisy effects. Notice and compare Figures 2 and 3.

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