Demand forecasting of fresh vegetable product by seasonal ARIMA model Online publication date: Sat, 21-Jun-2014
by Srikanth Sankaran
International Journal of Operational Research (IJOR), Vol. 20, No. 3, 2014
Abstract: Indian agriculture must remain responsive to managing change and meeting diverse demands of domestic and international stakeholders. Especially when dealing with vegetables with a short shelf life, successful forecasting can be an invaluable way to meet the above mentioned goals. In this paper, we forecast the daily demand for fresh vegetable product (onions) in a Mumbai wholesale market, based on historical data. Of the models developed and tested, a seasonal auto regressive integrated moving average (SARIMA) model outperformed other contenders in terms of forecasting accuracy on both in-sample and two out-of-sample datasets. Results show that the model can be used to forecast with a mean absolute percentage error (MAPE) of 14% which is considered acceptable for products with stochastic demand such as fresh vegetables. In addition to forecasting demand, this paper also aims to provide a practitioners view of ARIMA modelling using Stata that could be used for teaching/learning purposes.
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