Hybrid time-series models for forecasting agricultural commodity prices

Issue title: Machine Learning in Applied Statistics

Guest editors: Jong-Min Kim

Article type: Research Article

Authors: Mitra, Dipankar | Paul, Ranjit Kumar^*

Affiliations: ICAR – Indian Agricultural Statistics Research Institute, New Delhi, India

Correspondence: [*] Corresponding author: Ranjit Kumar Paul, ICAR – Indian Agricultural Statistics Research Institute, New Delhi 110012, India. E-mail: [email protected].

Abstract: Agricultural price forecasting has become a promising area of research in recent times. ARIMA model has been most widely used technique during the last few decades for this purpose. When the assumption of homoscedastic error variance is violated then ARCH/GARCH models are applied in order to capture the changes in the conditional variance of the time-series data. The ANN approach can also be applied in the field of forecasting of real time-series data successfully as an alternative to the traditional forecasting models. Real-world time-series data are rarely pure linear or nonlinear in nature, sometimes contain both the pattern together. In this situation a hybrid approach of combining the forecasts from a linear time-series model (ARIMA) and from a nonlinear time-series model (GARCH, ANN) has the better forecasting performance. The hybrid methodology namely ARIMA-GARCH and ARIMA-ANN have been applied for modelling and forecasting of wholesale potato price in Agra market of India. A comparative assessment has also been made in terms of Mean absolute percentage error (MAPE) and Root mean square error (RMSE) among the hybrid and their individual counterpart as far as forecasting is concerned. It is observed that ARIMA-ANN hybrid model outperforms the other combinations and individual counterpart for the data under consideration. R software package has been used for the data analysis.

Keywords: ARIMA, ANN, GARCH, hybrid model, potato price

DOI: 10.3233/MAS-170400

Journal: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 255-264, 2017