title: Multilayer stock forecasting model using fuzzy time series.
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.
authors.affiliation: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Hossein
authors.initials: H
authors.lastname: Javedani Sadaei
authors.affiliation: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Muhammad Hisyam
authors.initials: MH
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D006801:Humans; D017059:Models, Econometric; D008962:Models, Theoretical