 Demand forecasting of fresh vegetable product by seasonal ARIMA modelSrikanth Sankaran International Journal of Operational Research, 2014, vol. 20, issue 3, 315-330 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. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:20:y:2014:i:3:p:315-330 Access Statistics for this article More articles in International Journal of Operational Research from Inderscience Enterprises Ltd |
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