Forecasting exotic vegetable wholesale prices using time series analysis methods
Kate, Njoki Mbugua
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Vegetables are known to be highly perishable and seasonal in nature. Forecasting prices of vegetables is important to the Government of Kenya, the buyers and particularly to farmers as they can use this information to help them maximise their profits and minimise their losses. T11erefore, accurate forecasting of their prices requires the use of models that take the seasonal nature of vegetables into account. In this research paper, the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winter's Exponential Smoothing (HWES) models were used to forecast the wholesale prices of kales and cabbages in Nairobi, Kenya using monthly price data from January 2012 to December 2019. T11e Augmented Dickey Fuller (ADF) test showed that both the kales and cabbages price data were stationary hence no need for differencing. SARIMA(1,0,0)(1,1,1)12model was the best forecasting model for cabbages. This model was selected amongst other SARIMA models as it had the least Akaike Information Criterion (AIC) value. T11e Holt-Winter's Exponential Smoothing method was the best for kales. Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were the forecast performance measures used to select the best forecasting model for kales and cabbages.