YAPAY SİNİR AĞLARI İLE ÖNGÖRÜ MODELLEMESİ

YAPAY SINIR AĞLARI ILE ONGORU MODELLEMESI Ozet: Gelisen teknolojiye paralel olarak artan isleme ve hesaplama gucu ile birlikte, karmasik simulasyonlarin yapilmasi ve gelismis yapay zeka teknolojilerini kullanilarak temel kriterlere dayali olarak gelecege donuk ongorumleme modellemelerinin gerceklestirmesi mumkun hale gelmistir. Bu modellemelerin gerceklestirilmesini saglayan onemli bir uygulama alani ise “Yapay Sinir Aglari”dir. Bu calismada ongorumleme tekniklerinden zaman serisi yontemlerine giren “Box-Jenkins (ARIMA) Metodolojisi” ve “Yapay Sinir Aglari” yontemlerinin ongoruperformanslarini karsilastirarak en yuksek basariyi saglayan yontemin belirlenmesi ve belirlenen yontem yardimiyla 11 yil icin bir sirketten rastgele secilen dort urunun aylar itibariyle satis rakamlarinin tahmin edilmesi amaclanmistir. Calismanin uygulama bolumunde ongorumleme teknigi olarak Yapay Sinir Aglarinin kullaniminin daha basarili sonuclar urettigi sonucuna varilmistir. FORECASTING BY USING ARTIFICIAL NEURAL NETWORKS Abstract: Along with the processing and computation power increasing parallel with the developing technology, performing complex simulations and establishing forecasting models using developed artificial intelligence technologies based on the main criterions have been rendered possible. One important application field ensuring the possibility of these models is “Artificial Neural Networks”. In this study, it is aimed to determine the method providing the highest success by comparing the forecasting performances of the “Box-Jenkins (ARIMA) Methodology” and “Artificial Neural Networks” which are included in the time series methods of the forecasting techniques and to forecast with the determined method the sales values of three products choosen randomly from the products being produced in a company for 11 years are aimed. In the application part of the study it is reached to conclusion that to use Artificial Neural Networks as a forecasting method will give more successful results.

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