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Demand forecasting of fresh vegetable product by seasonal ARIMA model

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  • Srikanth Sankaran

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.

Suggested Citation

  • Srikanth Sankaran, 2014. "Demand forecasting of fresh vegetable product by seasonal ARIMA model," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 20(3), pages 315-330.
    • Handle: RePEc:ids:ijores:v:20:y:2014:i:3:p:315-330
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