Maize crop price prediction in Ghana using time series models

Isaac Osei, Benjamin Appiah, Makafui Nyamadi, Bill Frimpong, Elorm Kwaku Titiati

Abstract


ABSTRACT

The agribusiness has become very complex in recent years, and hence the importance of agricultural planning has increased. Crop producers can often base their decisions for crop production and selling on yield and price forecasts. Prediction of future crop selling prices is another important aspect in decision planning. (Wen, n.d.). In this research, the price of maize in Ghana was carefully studied. Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), Triple Exponential Smoothing (TES), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving-Average (SARIMA) modeling were done to find the best fit model to future predict the price of the maize crop in Ghana. The results of this study indicate that the DES model is the best fit model over other time series models considered in this paper.


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References


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DOI: https://doi.org/10.23954/osj.v10i1.3648

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