Abstract:
Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality
and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with
high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is
developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of
Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time
series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1)12 was identified as the best
model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then
estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in
Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3
showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.
