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Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices

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  • Abhishek Singh
  • G. C. Mishra

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

  • Abhishek Singh & G. C. Mishra, 2015. "Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 83-96, May.
    • Handle: RePEc:csb:stintr:v:16:y:2015:i:1:p:83-96
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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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    Cited by:

    1. Abdoulaye Camara & Wang Feixing & Liu Xiuqin, 2016. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(5), pages 231-231, April.

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