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

Abhishek  Singh; G. C.  Mishra

doi:https://doi.org/10.59170/stattrans-2015-005

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

Abhishek Singh Asstt. Prof., Department of Farm Engineering; Institute of Agricultural Sciences; Banaras Hindu University, Varanasi, India-221005. , G. C. Mishra Prof., Department of Farm Engineering; Institute of Agricultural Sciences; Banaras Hindu University, Varanasi, India-221005. Statistics in Transition new series, vol. 16, 2015, 1, pages: 83-96 Published online: 2 March 2015 https://doi.org/10.59170/stattrans-2015-005

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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.

KEYWORDS

forecasting, feed forward network, ARIMA, ANN.

REFERENCES

ADYA, M., COLLOPY, F., (1998). How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting, 17, 481-495.

BOX, G. E. P., JENKINS, G. M., (1976). Time Series Analysis: Forecasting and Control. revised ed. Holden-Day, San Francisco.

CHEN, S. T., YU, D. C., MOGHADDAMJO, A. R., (1992). Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE Transactions on Power Systems, 7, 3,1098-1105.

DE GOOIJER, J. G., HYNDMAN, R. J., (2006). 25 years of time series forecasting. International Journal of Forecasting. Elsevier, Vol. 22(3), 443-473.

KOHZADI, N., BOYD, M. S., KAASTRA, I., KERMANSHAHI, B. S., (1996). A comparison of artificial neural network and time series models for forecasting commodity price Neurocomputing, 10, 169-18.

MCCULLOCH, W. S., PITTS, W., (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115-133.

NEWBOLD, P., GRANGER C. W. J., (1974). Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society, A 137, 131-165.

PARK, D. C., EL-SHARKAWI, M. A., MARKS II, R. J., ATLAS, L. E.,DAMBORG, M. J., (1991). Electric load forecasting using an artificial neural network, IEEE Transactions on Power Systems, 62, 442-449.

RIPLEY, B., (1994). Neural Networks and Related Methods for Classification (with discussion). Journal of the Royal Statistical Society, B, 56, 409-456.

RUMELHART, D. E., HINTON, G. E., WILLIAMS, R. J., (1986). Learning Internal Representations by Error Propagation, in Parallel Distributed Processing: Exploration in the Microstructure of Cognition. Cambridge, MA: MIT Press., Vol. 1, 318-362.

TANG, Z., DE ALMEIDA, C., FISHCWICK, P. A., (1991). Time series forecasting using neural networks vs. Box Jenkins methodology. Simulation,57, 5, 303-310.

Website of Ministry of Consumer Affairs:http://fcainfoweb.nic.in/PMSver2/Reports/Report_Menu_web.aspx

ZHANG, G. P., (2003). Times series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-75.

ZOUA, H. F., XIAA, G. P., YANGC, F. T., WANGA, H. Y., (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70, 2913-2923.