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Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü = Forecasting of Turkey Inflation with Hybrid of Feed Forward and Recurrent Artifical Neural Networks

Author(s): N. Alp ERİLLİ | Erol EĞRİOĞLU | Ufuk YOLCU | Ç. Hakan ALADAĞ | V. Rezan USLU

Journal: Dogus University Journal
ISSN 1302-6739

Volume: 11;
Issue: 1;
Start page: 42;
Date: 2010;
Original page

Keywords: Öngörü | İleri Beslemeli Yapay Sinir Ağları | Geri Beslemeli Yapay Sinir Ağları | Forecasting | Feed Forward Neural Networks | Recurrent Neural Networks

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.
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