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Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method

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  • Amiri, Arshia
  • Bakhshoodeh, Mohamad
  • Najafi, Bahaeddin
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    Abstract

    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.

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    File URL: http://mpra.ub.uni-muenchen.de/34093/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 34093.

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    Date of creation: 13 Oct 2011
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    Handle: RePEc:pra:mprapa:34093

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    Related research

    Keywords: Price; Geostatistical model; Kiriging; Inverse distance weighting; Winter’s method; Adaptive neuro fuzzy inference system; Potatoes; Onions; Iran;

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    1. Joutz, Frederick L. & Trost, Robert P. & Hallahan, Charles B. & Clauson, Annette L. & Denbaly, Mark, 2000. "Retail Food Price Forecasting At Ers: The Process, Methodology, And Performance From 1984 To 1997," Technical Bulletins 33575, United States Department of Agriculture, Economic Research Service.
    2. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    3. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.
    4. Granger, C. W. J. & Newbold, P., 1976. "The use of R2 to determine the appropriate transformation of regression variables," Journal of Econometrics, Elsevier, vol. 4(3), pages 205-210, August.
    5. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    6. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
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