BOX-JENKINS YÖNTEMİYLE ÇİLEK SATIŞ FİYATLARI İÇİN TAHMİN MODELİ KURULMASI VE TAHMİN SONUÇLARININ DEĞERLENDİRİLMESİ

Taze tüketiminin yanı sıra gıda endüstrisinde birçok alanda hammadde olarak başrol oynayan çilek, geleneksel üretimin dışında, sera ve topraksız tarım üretimi ile yılın dört mevsimi ulaşılabilir bir meyvedir. Hasattan sonraki dönemde yola dayanıksız ve stoklama açısından riskli olan çilek, bu sebeple bölge ve mevsime göre fiyat farklılıkları göstermektedir. Bu fiyat farklılıkları hem üreticiyi hem de tüketici ve çileği hammadde olarak kullanan gıda endüstrisini de önemli ölçüde etkilemektedir. Haftalık çilek satış fiyatlarını kullanarak, Türkiye’deki haftalık çilek satış fiyatları için tahmin modeli geliştirmeyi amaçladığımız çalışmamızda, zaman serisi verileri trend veya mevsimsellik göstermediği için Box-Jenkins tahmin modelinden yararlanılmıştır. Yapılan analizler sonucunda 21 farklı ARIMA (p,d,q) modelleri arasından en başarılı tahmin sonucunu veren ARIMA(3,1,2) modeli seçilmiştir. Bu modele göre geleceğe yönelik 52 haftalık çilek fiyatı tahmini yapılmıştır.

Anahtar Kelimeler:

ARIMA, BOX-JENKINS, ÇİLEK FİYAT TAHMİNİ, TAHMİN MODELİ, TARIM EKONOMİSİ

ESTABLISHING A FORECAST MODEL FOR STRAWBERRY SALES PRICES BY BOX-JENKINS METHOD AND EVALUATION OF THE FORECAST RESULTS

Strawberry, which plays a leading role as a raw material in many fields in the food industry as well as its fresh consumption, is a fruit that can be reached all four seasons of the year with greenhouse and soilless agriculture production, apart from traditional production. Strawberry is not resistant to the road and is risky in terms of stocking in the post-harvest period, therefore shows price differences according to the region and season. These price differences significantly affect both the producer and the consumer and the food industry, which uses strawberries as raw materials. In our study, where we aimed to develop a forecast model weekly strawberry sales prices in Turkey using weekly strawberry sales prices, Box-Jenkins forecasting model was used because time series data do not show trends or seasonality. As a result of the analyzes made, among 21 different ARIMA(p,d,q) models, the ARIMA(3,1,2) model was chosen, which gave the most successful estimation result.According to this model, a 52-week strawberry price prediction was made for the future.

Keywords:

AGRICULTURE ECONOMICS, ARIMA, BOX-JENKINS, FORECAST MODEL, strawberry price forecast,

PDF

___

Referans1 Adanacıoğlu, H., and Yercan, M., (2012), An Analysis of Tomato Prices at Wholesale Level in Turkey: An Application of SARIMA Model, Custos e agronegocio, V.8, N.4, 52-75.
Referans2 Akın, M., and Peral Eyduran, S., (2017),Forecasting Harvest Area and Production of Strawberry Using Time Series Analyses, Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 34(3), 18-26.
Referans3 Aphinaya, M., Rathnayake, R. M. C. W. M., Sivakumar, S., Amarakoon, A. M. C.(2016), Price Forecasting of Mango Using SARIMA Model, Proceedings of the International Conference on the Humanitiesand the Social Sciences, University of Peradeniya, 3-8, Sri Lanka.
Referans4 Boz, F., and Hüseyinli, N.,(2019), Türkiye’de Muz Üretimi ve İthalatına Yönelik Bir Tahmin Modellemesi, Uygulamalı Bilimler Fakültesi Dergisi, Cilt: 1, Sayı:1-2, ss.63-82.
Referans5 Can, M.,(2009), İşletmelerde Zaman Serileri Analizi ile Tahmin, Doktora Tezi, İstanbul Üniversitesi, Sosyal Bilimler Üniversitesi, 252, İstanbul.
Referans6 Chaundhary, M., (2021), AI Aided Tools for Fresh Produce Yield and Price Forecasting Deep Learning Approaches, Masters Thessis, Univercity of Waterloo, 100, Canada.
Referans7 Darekar, A. And Reddy, A., (2017), Forecasting of Common Paddy Prices in India, Journal of Rice Research, Vol. 10, No.1, 71-75.
Referans8 Erdal, G.,(2006), Tarımsal Ürünlerde Üretim – Fiyat İlişkisinin Koyck Yaklaşımı İle Analizi (Domates Örneği), Dergipark, Cilt:2006, sayı:2,21-28.
Referans9 Erdoğan, M. A., (2021), Türkiye’de Şeftali Fiyatlarının Analizi ve Fiyatların Box-Jenkins Yöntemiyle Tahmini, Yüksek Lisans Tezi, Uludağ Üniversitesi, 83, Bursa.
Referans10 Eşidir, K.A., and Metin, S.,(2021), Türkiye Domates İhracatının Yapay Sinir Ağları Yöntemi Kullanılarak Tahmin Edilmesi, 5th International Mardin Artuklu Scientific Researches Conference.
Referans11 Evans, E.A. and Nalampang, S.,(2019), Forecasting Price Trends in the U.S. Avocado, (Persea americana Mill.) Market, Journal of Food Distribution Research 40(2), 37-46.
Referans11-2 FAOSTAT,(2022), Food and Agriculture Organization of the United Nation.
Referans13 Garcia, W.J.P., Velázquez, R.V.O., Pacheco, I.T., Jiménez, C.A.C., (2019), Price Forecasting and Span Commercialization Opportunities for Mexican Agricultural Products, Agronomy, 9, 826.
Referans14 Garde, Y.A., Chavda, R.R., Thorat, V.S., Pisal, R.R.(2021), Forecasting of Area, Productivity and Prices of Mango in Navsari district, Gujarat, Journal of Crop and Weed, 17(3): 17-28.
Referans15 Güler, D., Uçar, K., Engindeniz, S.,(2021), Türkiye’de Kayısı Üretiminin ARIMA Modeli ile Tahmini, Turkish Journal of Agricultural Economics, Cilt:27, Sayı:2, 55-62.
Referans16 Ibrahim, M., and Florkowski, W. J., (2007), Forecasting U.S. Shelled Pecan Prices: A Cointegration Approach, The Southern Agricultural Economics Annual Meeting, 11, Alabama.
Referans17 Ibrahim, M., and Florkowski, W. J., (2009), Forecasting Price Relationships among U.S Tree Nuts Prices, The Southern Agricultural Economics Association Annual Meeting, 17, Atlanta, Georgia.
Referans18 Intaramo, R., and Yimnak, K., (2018), The Forecasting Efficiency of Fuzzy Time Series Model Based on Fuzzy Inverse for Forecasting Thailand Fruit Price, Pathumwan Academic Journal, Vol.8, No.23, 13-22.
Referans19 Jadhav, V., Reddy, B.V. C., Gaddi, G. M.,( 2017), Application of ARIMA Model for Forecasting Agricultural Prices, Journal of Agricultural Science and Technology, Vol.19:981-992.
Referans2P Kaynar, O., and Taştan, S.,(2009), Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA Modellerinin Karşılaştırılması, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı:33, 161-172.
Referans21 Kaya, E,(2019), Zaman Serileri Analizinde Box-Jenkins Yöntemi ile Savunma Sanayi Verileri Üzerine Bir Uygulama, Yüksek Lisans Tezi, Karamanoğlu Mehmetbey Üniversitesi, Sosyal Bilimler Enstitüsü, 168, Karaman.
Referans22 L. Maskey, M., B. Pathak, T., K. Dara, S., (2019), Weather Based Strawberry Yield Forecasting at Feild Scale Using Statistical and Machine Learning Models, Atmosphere , 10, 378.
Referans23 Li, G., Xu, S., Li, Z., (2010), Short-Term Price Forecasting for Agro-products Using Artifical Neural Networks, International Conference onn Agricultural Risk and Food Security, Agriculture and Agricultural Science Procedia 1, 278-287.
Referans24 Liu, N., and Yu, J., (2019), Raw Grain Price Forecasting with Regression Analysis, International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019), Advances in Computer Science Research, volume 91, 372-378.
Referans25 Mehmood, Q., Sial, M. H., Riaz M., Shaheen, N.,(2019), Forcasting the Production of Sugarcane Crop of Pakistan fort he Year 2018-2030, Using Box-Jenkins Methodology, Journal of Animal and Plant Sciences, 29(5), 1396-1401.
Referans26 Mishra, G.C., and Singh, A., (2013), A Study on Forecasting Prices of Groundnut Oil in Delhi by Arima Methodology and Artificial Neural Networks, Agris on-line Papers in Economics and Informatics, Vol. V, Number, 2, 25-34.
Referans27 Nahmias, S., and Olsen, T.L., (2015), Production and Operations Analysis, Seventh Edition, Waveland Press,Inc.
Referans28 NİGEP, 2012. Ziraat Alanı Modern Çilek Yetiştirme Teknikleri Modülü. 37, Bişkek.
Referans29 Okwuchi, I.,(2020), Machine Learning Based Models for Fresh Produce Yield and Price Forcasting for Strawberry Fruit, Master Thessis, Univercity of Waterloo, 90, Canada.
Referans30 Özer, O. O., ve Gül Yavuz, G., (2014), Box-Jenkins Modeli Yardımıyla Fındık Fiyatının Tahmini, XI. Ulusal Tarım Ekonomisi Kongresi, 1689-1694, Samsun.
Referans31 Rathod, S., Mishra, G. C., Singh, K. H., (2017), Hybrid Time Series Models for Forecasting Banana Production in Karnataka State, India, Journal of the Indian Society of Agricultural Statistics, 71(3), 193-200.
Referans32 Rueangrit, P., Jatuporn, C., Suvanvihok, V., Wanaset, A., (2020), Forecasting Production and Export of Thailand’s Durian Fruit: An Empirical Study Using the Box-Jenkins Approach, Humanities and Sciences Letters, Vol.8, No.4, pp.430-437.
Referans33 Sukiyono, K., Arianti, N.N., Sumantri, B., Romdhon, M.M., Suryanty, M., Adiprasetyo, T., (2021), A Model Selection for Price Forecasting of Crude Palm Oil and Fresh Fruit Bunch Price Forecasting, Iraqi Journal of Agricultural Science, 52(2):479-490.
Referans34 Suppalakpanya, K., Nikhom, R., Booranawong, T., Booranawong, A.,(2019), Forecasting oil palm and crude palm oil data in Thailand using exponential time-series methods, Engineering and Applied Science Research, 46(1),44-55. Referans35 TÜİK,(2022), Türkiye İstatistik Kurumu.
Referans36 Ullah, A., Khan, D., Zheng, S.,(2018), Forecasting of Peach Area and Production Wise Econometric Analysis, The Journal of Animal and Plant Sciences, 28(4), 1121-1127.
Referans37 Uysal, O., Subaşı, O.S., Yaşar, B., (2016), Türkiye Muz Üretim ve İthalatının Box-Jenkins ve Delphi Yöntemleri ile Tahmini, XII. Tarım Ekonomisi Konferansı, 1275-1286.
Referans38 Yıldız, M.C., and Atış, E.,(2019), Türkiye Organik Kuru İncir İhraç Fiyatının ARMA Yöntemi ile Tahmini, Tarım Ekonomisi Dergisi, Cilt:25, Sayı:2, 141-147.
Referans39 Zhang, D., Chen, S., Xia, Q., (2020), Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons, IEEE Access, Vol.8, 28197-28209.
Referans40 https://arastirma.tarimorman.gov.tr(01.02.2022), Tarım Ürünleri Piyasa Raporu, TEPGE, Çilek, Haziran 2021.