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Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series

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  • Awe, Olushina Olawale
  • Dias, Ronaldo

Abstract

With the vast popularity of the deep learning models in the engineering and mathematical fields, Artificial Neural Networks (ANN) have recently attracted significant research applications in agriculture, economics, informatics and finance. In this paper, we use a deep learning method to capture and predict the unknown complex nonlinear characteristics of agricultural output based on autoregressive artificial neural network, using Nigeria as a case study. Using the proposed model, shocks in agricultural output is analyzed and modeled using data obtained for a period of forty years (1980-2019), and compared with analyses obtained from the autoregressive integrated moving average model (ARIMA). This result is significant because it justifies the superiority of the hybrid ANN model over the traditional Box-Jenkins methodology for forecasting non-stationary time series. The empirical results show that the proposed autoregressive ANN model achieves an improved forecasting accuracy over the traditional Box-Jenkins ARIMA method. It is further proposed that various types of artificial neural networks would be useful in forecasting and solving relevant tasks and problems widely defined in global agricultural production.

Suggested Citation

  • Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.
    • Handle: RePEc:ags:aolpei:330100
    DOI: 10.22004/ag.econ.330100
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    References listed on IDEAS

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    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
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    4. Awe, O. O. & Akinlana, D. M. & Yaya, O. S. & Aromolaran, O., 2018. "Time Series Analysis of the Behaviour of Import and Export of Agricultural and Non-Agricultural Goods in West Africa: A Case Study of Nigeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(2).
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    8. Olushina Olawale Awe & Luis Alberiko Gil-Alana, 2019. "Time series analysis of economic growth rate series in Nigeria: structural breaks, non-linearities and reasons behind the recent recession," Applied Economics, Taylor & Francis Journals, vol. 51(50), pages 5482-5489, October.
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    Cited by:

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