Title: Demand forecasting of fresh vegetable product by seasonal ARIMA model

Authors: Srikanth Sankaran

Addresses: Her Majesty's Revenue and Customs, London, SW1A 2BQ, UK

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.

Keywords: time series; auto regressive integrated moving average; ARIMA; demand forecasting; India; Stata; fresh vegetables; fresh vegetable products; seasonal models; modelling; onions.

DOI: 10.1504/IJOR.2014.062453

International Journal of Operational Research, 2014 Vol.20 No.3, pp.315 - 330

Received: 25 Feb 2012
Accepted: 26 Nov 2012
Published online: 21 Jun 2014 *

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