FİNANSAL VERİSETLERİ İÇİN BOZKURT OPTİMİZASYON TEMELLİ GERİ BESLEMELİ BULANIK ÇIKARIM FONKSİYONLARI

Zaman serisi modelleri, tıp, mühendislik, işletme, ekonomi ve finans gibi birçok alanda, önceki dönemlerden gözlem değerleri yardımıyla tahminler yapmak amacıyla yaygın olarak kullanılmaktadır. Bu nedenle, özellikle alternatif/olasılık dışı yöntemler kullanılarak, zaman serisi tahmin performanslarını geliştirmek için birçok çaba vardır. Bu çalışmada, zaman serisi veri kümesindeki doğrusal olmayan yapının üstesinden gelebilmek için, Bozkurt optimizasyon (GWO) temelli Otoregresif hareketli ortalama (ARMA) modeli ile tip-1 bulanık fonksiyonların (T1FFs) birleştirilmesiyle yeni bir tahmin yaklaşımı önerilmiştir. GWO'nun, arama boyunca keşif ve uygun stabiliteye hızlı ulaşması, daha az depolama gereksinimleri ve hızlı yakınsama gibi diğer yöntemler üzerindeki üstünlükleri göz önüne alındığında, kare hatalarının toplamını en aza indirgemek için geribeslemeli T1FFs yönteminin katsayılarının tahmini GWO ile elde edilmesi uygun görüşmüştür. Beş farklı gerçek veri kümesinde önerilen yöntemin ve mevcut birkaç tahmin yönteminin karşılaştırılması gerçekleştirilmiştir. Sonuçlar, önerilen yöntemin, ortalama mutlak yüzde hataları ve kök ortalama kare hataları ile birlikte daha iyi çalışma süresi açısından çoğu zaman daha iyi tahminler ürettiğini göstermektedir

Anahtar Kelimeler:

Tip-1 Bulanık Fonksiyonlar, Boz-kurt Optimizasyonu, Öngörü, Otoregresif Hareketli Ortalamlar

GREY WOLF OPTIMIZER BASED RECURRENT FUZZY REGRESSION FUNCTIONS FOR FINANCIAL DATASETS

Time series models are used extensively in many fields, such as medicine, engineering, business, economics and finance, with the aim of making forecasts through the help of observation values from previous periods. Therefore, there are many efforts to improve time series forecasting performances in the recent literature, mainly using alternative/non-probabilistic methods. In the present study, a novel forecasting approach has been proposed by combining the type-1 fuzzy functions (T1FF) with the Autoregressive moving average (ARMA) model based on grey wolf optimizer (GWO) in order to be able to overcome the nonlinear structure in time series dataset. Considering the superiorities of GWO over other methods, such as less storage requirements and rapid convergence by striking the proper stability between the exploration and exploitation throughout the search, estimation of the coefficients of the R-T1FFs method obtained through GWO to minimize the sum of squared errors (SSE). Comparison of the proposed method and several existing forecasting methods has been performed on five real world time series datasets. The results indicate that the proposed method produces better forecasts most of the time in the terms of mean absolute percentage errors and root mean square errors along with the better running time.

Keywords:

Type-1 Fuzzy Functions, Grey Wolf Optimizer, Autoregressive Moving Average, Forecasting,

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