Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series
DOI:
https://doi.org/10.7160/aol.2022.140401How to Cite
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.References
Abhishek, K., Singh, M. P., Ghosh, S. and Anand, A. (2012) "Weather forecasting model using artificial neural network", Procedia Technology, Vol. 4, pp. 311-318. E-ISSN 2212-0173. DOI: https://doi.org/10.1016/j.protcy.2012.05.047
Akinkunmi, M. A. (2017) "Nigeria's economic growth: Past, present and determinants", Journal of Economics and Development Studies, Vol. 5, No. 2, pp. 31-46. E-ISSN 2334-2390, ISSN 2334-2382. DOI: https://doi.org/10.22437/ppd.v8i2.9106
Anderu, K. S. and Omotayo, E. O. (2020) "Agricultural output and government expenditure in Nigeria", Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, Vol. 8, No. 2, pp. 101-110. E-ISSN 2355-8520.
Awe, O. O. and Gil-Alana, L. A. (2019) "Time series analysis of economic growth rate series in Nigeria: structural breaks, non-linearities and reasons behind the recent recession", Applied Economics, Vol. 51, No. 50, pp. 5482-5489.ISSN 00036846. DOI: https://doi.org/10.1080/00036846.2019.1613513
Awe, O., Okeyinka, A. and Fatokun, J. O. (2020) "An Alternative Algorithm for ARIMA Model Selection", In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), IEEE, pp. 1-4. DOI: https://doi.org/10.1109/ICMCECS47690.2020.246979
Awe, O. O., Akinlana, D. M., Yaya, O. S. and 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, Vol. 10, No. 2, pp. 15-22. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2018.100202
Awe, O. O., Ayeni, O. C., Sanusi, G. P. and Oderinde, L. O. (2021) "A comparative time series analysis of crude mortality rate in the BRICS countries", BRICS Journal of Economics, Vol. 2, No. 2, pp. 17-32. ISSN 2712-7508. DOI: https://doi.org/10.38050/2712-7508-2021-2-2
Ayinde, O. E., Ilori, T. E., Ayinde, K. and Babatunde, R. O., (2015) "Analysis of the behaviour of prices of major staple foods in West Africa: A case study of Nigeria", AGRIS on-line Papers in Economics and Informatics, Vol. 7, No. 4, pp. 3-17. ISSN 1804-1930.
Bergmeir, C. and Benítez, J. M. (2012) "On the use of cross-validation for time series predictor evaluation", Information Sciences, Vol. 191, pp. 192-213. ISSN 00200255. DOI: https://doi.org/10.1016/j.ins.2011.12.028
Bergmeir, C., Hyndman, R. J. and Koo, B. (2018) "A note on the validity of cross-validation for evaluating autoregressive time series prediction", Computational Statistics & Data Analysis, Vol. 120, pp. 70-83. ISSN 0167-9473. DOI: https://doi.org/10.1016/j.csda.2017.11.003
Central Bank of Nigeria (2014) "Statistical Bulletin". [Online]. Available: http://statistics.cbn.gov. ng/cbnonlinestats [Accessed: Sept. 21, 2022].
Christiaensen, L, Demery, L. and Kuhl, J. (2007) "The role of agriculture in poverty reduction: An empirical perspective", World Bank Policy Research Working Paper, No. 4013.
Ewetan, O., Fakile, A., Urhie, E. S. and Odunatan, E. (2017) "Agricultural output and economic growth in Nigeria", Journal of African Research in Business & Technology ". E-ISSN 2165-9443.
Faraway, J. and Chatfield, C. (1998) "Time series forecasting with neural networks: a comparative study using the airline data", Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 47, No. 2, pp. 231-250. ISSN 00359254. DOI: https://doi.org/10.1111/1467-9876.00109
Falola, T. and Heaton, M. (2008) "A history of Nigeria", New York: Cambridge University Press; 2008, 370 p. ISBN 052168157X. DOI: https://doi.org/10.1017/CBO9780511819711
Hloušková, Z., Ženíšková, P. and Prášilová, M. (2018) "Comparison of Agricultural Costs Prediction Approaches", AGRIS on-line Papers in Economics and Informatics, Vol. 10, No. 1, pp. 3-13. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2018.100101
Igbokwe, E. M. (2005) "Concepts in rural and agricultural sociology", Agricultural Extension in Nigeria, pp. 91-100. ISSN 1119944X.
Kharin, S. (2018) "Price Transmission Analysis: The case of milk products in Russia", AGRIS on-line Papers in Economics and Informatics, Vol. 10, No. 1, pp. 15-23. ISSN 1804-1930. DOI: https://doi.org/10.7160/aol.2018.100102
Kujawa, S. and Niedbała, G. (ed.) (2021) "Artificial Neural Networks in agriculture", Agriculture, Special issue, Vol. 11, No. 6, p. 497. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture11060497
Li, Y and Chao, X. (2020) "ANN-Based Continual Classification in Agriculture", Agriculture, Vol. 10, No. 5, p.178. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture10050178
Matthew, A. and Mordecai, B. D. (2016) "The impact of public agricultural expenditure on agricultural output in Nigeria (1981-2014)", Asian Journal of Agricultural Extension, Economics & Sociology, Vol. 11, No. 2, pp. 1-10. E-ISSN 2320-7027. DOI: https://doi.org/10.9734/AJAEES/2016/25491
Mensi, W., Tiwari, A., Bouri, E., Roubaud, D. and AI-Yahyaee, K. H. (2017) "The dependence structure across oil, wheat, and corn: a wavelet-based copula approach using implied volatility indexes", Energy Economics, Vol. 66, pp. 122-139. ISSN 0140-9883. DOI: https://doi.org/10.1016/j.eneco.2017.06.007
Niedbała, G., Kurasiak-Popowska, D., Stuper-Szablewska, K. and Nawracała, J. (2020) "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain", Agriculture, Vol. 10, p. 127. ISSN 2077-0472. DOI: https://doi.org/10.3390/agriculture10040127
Rakhmatuiln, I., Kamilaris, A. and Andreasen, C. (2021) "Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review", Remote Sensing, Vol. 13, No. 21, p. 4486. ISSN 2072-4292. DOI: https://doi.org/10.3390/rs13214486
Rodrigues, P. C., Awe, O. O., Pimentel, J. S. and Mahmoudvand, R. (2020) "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks", Stats, Vol. 3, No. 2, pp. 137-157. ISSN 2571-905X. DOI: https://doi.org/10.3390/stats3020012
