Petrol Hizmetleri Borsa Yatırım Fonu (ETF) Tahmini için Gramian Açısal Alanın Evrişimsel Sinir Ağı Analizi

Abstract

Petrol fiyatlarının tahmini, alınacak ekonomik kararlar ve oluşturulacak mali politikalar açısından hem ülkeler hem de şirketler için önemlidir. Ancak finansal fiyat dalgalanmaları doğası gereği doğrusal olmayan, karmaşık ve belirsizdir. Bu nedenlerden dolayı petrol fiyatlarının tahmini zor bir problemdir. Literatürde, petrol fiyatlarını tahmin etmek için istatistiksel ve makine öğrenimi yöntemleri kullanılmıştır. Ancak bu çalışmaların çoğunda petrol fiyatları genellikle zaman serisi olarak temsil edilmiştir. Bu çalışmada, petrol borsa yatırım fonu (ETF) verileri, görüntülerin temsil gücünden faydalanmak için Gramian Açısal Alan (GAF) yöntemi kullanılarak 2 boyutlu görüntü olarak temsil edilmiş ve daha sonra bu görüntü veri kümelerini analiz etmek için AlexNet ve VGG16 evrişimsel sinir ağı (CNN) mimarileri kullanılmıştır. Mevcut ve önerilen GAF-AlexNet ve GAF-VGG16 modellerinin performanslarını test etmek için enerji şirketlerine yatırım yapan bir fon olan VanEck Petrol Hizmetleri ETF'sine (OIH) ait 2016 ve 2022 dönemlerini kapsayan bir veri kümesi kullanılmıştır. Deneysel değerlendirmeler, önerilen modellerin umut verici sonuçlar verdiğini göstermektedir. Bulgular, tahmin modelinin bir ticaret sistemine entegre edilmesinin, araştırmacılara ve yatırımcılara bir karar destek sistemi olarak değerli bilgiler sağlayabileceğini göstermektedir.

Keywords

• : Derin Öğrenme
• Zaman Serisi Analizi
• ETF
• Gramian Açısal Alan (GAA)
• Fiyat Tahmini.

Convolutional Neural Network Analysis of the Gramian Angular Field for Oil Services Exchange Traded Fund (ETF) Prediction

Abstract

Prediction of oil prices is important for both countries and companies in terms of economic decisions to be made and financial policies to be created. However, due to the nature of financial price fluctuations, they are non-linear, complex, and uncertain. Because of this reasons, prediction of oil prices is a difficult problem. In the literature, statistical and machine learning methods have been used to predict oil prices. However, in most of these studies, oil prices were usually represented as time series. In this study, oil services Exchange-traded fund (ETF) data is represented as a 2D image using Gramian Angular Field (GAF) method, in order to benefit from the representation power of images and then AlexNet and VGG16 convolutional neural network (CNN) architectures are used to analyze this image datasets. To test the performances of existing and the proposed GAF-AlexNet and GAF-VGG16 models, a dataset covering period of 2016 and 2022 belonging to the VanEck Oil Services ETF (OIH), a fund that invests in energy companies, was used. Experimental evaluations show that the proposed models gave promising results. The findings suggest that integrating the predictive model into a trading system can provide valuable insights to researchers and investors as a decision support system.

Keywords

• Deep Learning
• Time Series Analysis
• ETF
• Gramian Angular Field (GAF)
• Price Prediction.

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