YSA DVM ve Karar Ağacı ile Tarımsal Emtiaların Fiyat Endekslerinin Tahminlenmesi: Kuru Üzüm Örneği
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Date
2018
Authors
Burcu KARAÖZ
IKBAL ECE DIZBAY
sevkinaz Gumusoglu
Ercin Guducu
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Abstract
Emtia fiyat endekslerinin başarılı bir şekilde tahminlenmesi ekonomik aktörlere doğru alım satım kararları verebilmeleri için fayda sağlamaktadır. Türkiye’deki ticaret borsalarında işlem gören tarımsal ürünlerden biri olan kuru üzüm fiyatlarının oynak değişkenler kullanılarak tahminlenmesinin incelendiği çalışmada üç temel soru üzerinde durulmuştur. İç karışıklığın yüksek olduğu ülkelerde sosyal ve politik olaylar kuru üzüm fiyatlarını etkiler mi? Oynaklığı yüksek olan değişkenler kullanılarak kuru üzüm fiyat endeksleri tahminlenebilir mi? Son olarak bu tip bir çalışmada Yapay Sinir Ağları (YSA) Karar Ağacı ve Destek Vektör Makineleri (DVM) yöntemlerinden hangisinin tahmin performansı daha yüksektir? Bu amaçla oluşturulan tahmin modeline YSA KA ve DVM yöntemleri uygulanmış ve yöntemlerin tahmin performansları karşılaştırılmıştır. Uygulama sonuçları oynak değişkenler ile sosyal ve politik olayların kuru üzüm fiyatlarının tahminlenmesinde kullanılabileceğini ve ilgili modelde DVM yönteminin en yüksek doğruluk oranını verdiğini göstermiştir.
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Ahmed S. (2008). Aggregate economic variables and stock markets in India. International Research Journal of Finance and Economics 14 141-164.Azadeh A. Moghaddam M. & Khakzad M. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Comput. Ind. Eng. 62(2): 421–430.Baffes J. & Haniotis T. (2016). What explains agricultural price movements? Journal of Agricultural Economics 67(3) 706-721.Chen A.-S. Leung M. T. & Daouk H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index . Computers & Operations Research 30(6) 901-924.Chen Y. C. Rogoff K. & Rossi B. (2010). Predicting agri-commodity prices: An asset pricing approach. SSRN.Cortes C. & Vapnik V. (1995). Support-vector networks. Machine learning 20(3) 273-297.Dean J. (2014). Big data data mining and machine learning: value creation for business leaders and practitioners. New Jersey: John Wiley & Sons. Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE 92(5) 771-805.Duch W. Setiono R. & Zurad J. (2004). Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE 92(5) 771- 805.Gargano A. & Timmermann A. (2014). Forecasting commodity price indexes using macroeconomic and financial predictors. International Journal of Forecasting 30(3) 825-843.Groen J. & Pesenti P. A. (2011). Commodity prices commodity currencies and global economic developments . In T. Ito & A. Rose Commodity prices and markets East Asia Seminar on Economics (Vol. 20 pp. 15–42 ). Chicago: University of Chicago.Haidar I. & Wolff R. (2009). Forecasting crude oil price. Retrieved from http://www.usaee.org/usaee2011/ submissions/ OnlineProceedings/Forecasting%20 Crude%20Oil%20Price%20%28Revisited%29.pdf.Hong H. & Yogo M. (2012). What does futures market interest tell us about the macroeconomy and asset prices? Journal of Financial Economics 105(3) 473–490.Hu J. W.-S. Hu Y.-C. & Lin R. R.-W. (2012). Applying neural networks to prices prediction of crude oil futures. Mathematical Problems in Engineering.Jammazi R. & Aloui C. (2012). Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling 34: 828–841. Energy Econ. 34(3) 828-841.Jha G. K. & Sinha K. (2014). Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications 24(3) 563- 571.Kara Y. Boyacioglu M. & Baykan Ö. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications 38(5) 5311-5319.Kohzadi N. Bahman M. S. & K I. (1996). Comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing 10(2) -.Larose D. & Larose C. (2015). Data mining and predictive analytics. New Jersey: John Wiley & Sons.Li G.-q. Xu S.-w. & Li Z.-m. (2010). Short-term price forecasting for agro-products using artificial neural networks. Agric. Sci. Procedia 1 278–287.Malliaris A. & Malliaris M. (2013). Are oil gold and the euro inter-related? Time series and neural network analysis. Review of Quantitative Finance and Accounting 40(1) 1-14.Mensi W. Beljid M. Boubaker A. & Managi S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies food and gold. Economic Modelling 32 15-22.Mitchell T. (1997). Machine Learning. New York: McGrawHill.Parisi A. (2008). Forecasting gold price changes: rolling and recursive neural network models. Journal of Multinatl. Financ. Manag 18(5) 477–487.Quinlan J. (1993). Programs for Machine Learning. California.: Morgan Kaufmann.Samanta S. & Zadeh A. (2012). Co-movements of oil gold the US dollar and stocks. Modern Economy 3(1) 111-117.Scholkopf B. & Smola A. (2001). Learning with kernels: support vector machines regularization optimization and beyond. MIT press.Shannon C. (1951). Prediction and entropy of printed English. Bell system technical journal 30(1) 50-64.Sujit K. S. & Kumar R. (2011). Study on dynamic relationship among gold price oil price exchange rate and stock market returns. International Journal of Applied Business and Economic Research 9(2) 145-165.United States Department of Agriculture. (2017 10 10). Retrieved from Market and Trade Data: https:// apps.fas.usda.gov/psdonline/app/index.html#/ app/topCountriesByCommodityWang Y. & Chueh Y. (2013). Dynamic transmission effects between the interest rate the US dollar and gold and crude oil prices. Economic Modelling 30 792-798.Yakut E. & Gemici E. (2017). LR C5. 0 CART DVM Yöntemlerini Kullanarak Hisse Senedi Getiri Sınıflandırma Tahmini Yapılması ve Kullanılan Yöntemlerin Karşılaştırılması: Türkiye’de BIST’de Bir Uygulama. Ege Akademik Bakis 17(4) 461-479.Ye M. Zyren J. & Shore J. (2002). Forecasting crude oil spot price using OECD petroleum inventory levels. International Advances in Economic Research 8(4) 324-333.Zou H. Xia G. Yang F. & Wang H. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing 70(16- 18) 2913–2923.
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Volume
18
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4
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579
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