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Application of Box-Jenkins Method and Artificial Neural Network Procedure for Time Series Forecasting of Prices

Author

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  • Singh Abhishek

    (Department of Farm Engineering, ; Institute of Agricultural Sciences, ; Banaras Hindu University, Varanasi, India)

  • Mishra G. C.

    (Department of Farm Engineering, ; Institute of Agricultural Sciences, ; Banaras Hindu University, Varanasi, India)

Abstract

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.

Suggested Citation

  • Singh Abhishek & Mishra G. C., 2015. "Application of Box-Jenkins Method and Artificial Neural Network Procedure for Time Series Forecasting of Prices," Statistics in Transition New Series, Statistics Poland, vol. 16(1), pages 83-96, March.
    • Handle: RePEc:vrs:stintr:v:16:y:2015:i:1:p:83-96:n:7
    DOI: 10.21307/stattrans-2015-005
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    References listed on IDEAS

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    1. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
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