FİNANSAL VERİSETLERİ İÇİN BOZKURT OPTİMİZASYON TEMELLİ GERİ BESLEMELİ BULANIK ÇIKARIM FONKSİYONLARI

Yıl 2020, Cilt: 15 Sayı: 54, 350 - 366, 30.07.2020

Nihat Tak

Öz

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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