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Citation Information : Statistics in Transition New Series. Volume 16, Issue 1, Pages 83-96, DOI: https://doi.org/10.21307/stattrans-2015-005
License : (CC BY 4.0)
Published Online: 31-October-2017
Forecasting of prices of commodities, especially those of agricultural commodities, is very difficult because they are not only governed by demand and supply but also by so many other factors which are beyond control, such as weather vagaries, storage capacity, transportation, etc. In this paper time series models namely ARIMA (Autoregressive Integrated Moving Average) methodology given by Box and Jenkins has been used for forecasting prices of Groundnut oil in Mumbai. This approach has been compared with ANN (Artificial Neural Network) methodology. The results showed that ANN performed better than the ARIMA models in forecasting the prices.