Amiri, Arshia and Bakhshoodeh, Mohamad and Najafi, Bahaeddin (2011): Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method.
Download (161kB) | Preview
This paper, we studied the ability of geostatistical models (ordinary kriging (OK) and Inverse distance weighting (IDW)), adaptive neuro-fuzzy inference system (ANFIS) and Winter method for prediction of seasonality in prices of potatoes and onions in Iran over the seasonal period 1986_2001. Results show that the best estimators in order are winter method, ANFIS and geostatistical methods. The results indicate that Winter and ANFIS had powerful results for prediction the prices while geostatistical models were not useful in this respect.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method|
|Keywords:||Price; Geostatistical model; Kiriging; Inverse distance weighting; Winter’s method; Adaptive neuro fuzzy inference system; Potatoes; Onions; Iran|
|Subjects:||Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics > Q1 - Agriculture
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
|Depositing User:||Arshia Amiri|
|Date Deposited:||13. Oct 2011 18:04|
|Last Modified:||13. Feb 2013 05:44|
Armstrong, J.S., Green, K.C., 2005. Demand Forecasting: Evidence-based Methods, Monash University, Department of Econometrics and Business Statistics, Working Paper 24/05, ISSN 1440-771X.
Black, K., 1997. Business statistics, contemporary decision making, 2nd Ed., West Publishing Company.
Bishop, T.F.A., McBratney A.B., 2001. A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma, 103, 149-160.
Burgos, P., Madejon, E., Perez-de-Mora, A. and Cabrera, F., 2006. Spatial variability of the chemical characteristics of a trace-element-contaminated soil before and after remediation. Geoderma, 130(1-2), 157-175.
Chiu, S.L, 1994. Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems, 2, 267-278.
Cressie, N., 1985. Fitting variogram models by weighted least squares. Mathematical Geology, 17, 563-586.
Cressie, N.A.C., 1991. Statistics for Spatial Data. John Wiley, New York, USA.
David, M., 1977. Geostatistical Ore Reserve Estimation, Elsevier, Scientific Publishing Co., Amsterdam, The Netherlands.
Dieng, A., 2008. Alternative Forecasting Techniques for Vegetable Prices in Senegal, Revue sénégalais de recherches agricoles et agroallementalress, 1 (3), 5-10.
Duffera, M., White, J.G. and Weiz, R., 2007.l Spatial variabilityof Southeastern U.S. Coastal Plain soil physical propreties: implications for site-specific management. Geoderma, 137, 327-339.
Gama Design Software, 2004. GSp Version 5.1. Geostatistics for the Environmental Sciences, User’s guide. Gama Design Software, LLC (160 pp.).
Granger, C.W.J., Newbold, P., 1976. The use of R2 to determine the appropriate transformation of regression variables. Journal of Econometrics, 4(3), 205-210.
Hill, T., Marquez, L., O'Connor, M. and Remus, W., 1994, Artificial Neural Network Models for Forecasting and Decision Making. International Journal of Forecasting, 10, p.5-15.
Isaaks, E.H., Srivstava, R.M., 1989. Applied Goestatistics. New York Oxford University Press, pp: 257-259.
Jang, J.R., 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System. Man and Cybernetics. 23(3), 665-685.
Joutz, F.L., Trost, R.P., Hallahan, C., Clauson, A., Denbaly, M., 2000. Retail food price forecasting at ERS: the process, methodology, and performance from 1984 to 1997. Economic Research Service, U.S. Department of Agriculture. Technical Bulletin No. 1885.
Krige, D.G., 1981, Lognormal-de Wijsian geostatistics for ore evaluation. South African Institute of Mining and Metallurgy Monograph Series. Geostatistics I. South Africa Institute of Mining and Metallurgy, Johannesburg, South Africa.
Mabit, L., and Bernard, C., 2007. Assessment of spatial distribution of fallout radionuclides through geostatistics concept. Journal of Environmental Radioactivity, 97, 206-219.
Mathworks, 2001. “Fuzzy Logic Toolbox, for use with MatLab.” User’s Guide, Version 2.
Robinson, T.P. and Metternicht, G., 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computer and Electronics in Agriculture, 50, 97-108.
Takagi, T., Sugeno, M., 1983. Derivation of fuzzy control rules from human operator’s control actions. Proc. Of the IFAC Symp. on Fuzzy Information. Knowledge Representation and Decision Analysis, 55-60.
Taylor, J.W., 2003. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19, 715-725.
Umar, M.A., 2007. Comparative study of Holt-Winter, double exponential and the linear trend regression models, with application to exchange rates of the naira to dollar. Research Journal of Applied Sciences, 2(5), 633-637.
Van Kuilemberg, J., De Gruitjer, J., Marsman, B., Bouma, J., 1982. Accuracy of spatial interpolation between point data on soil moisture supply capacity, compared with estimates from mapping units. Geoderma, 27, 311-325.
Voltz, M., Webster, R., 1990. A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Journal of Soil Science. 41, 473-490.
Zou, H., Yang, Y., 2004. Combining time series model for forecasting. International Journal of Forecasting, 20, 69-84.