IDEAS home Printed from https://ideas.repec.org/a/ags/aolpei/330100.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/330100/files/552_agris-on-line-4-2022-awe-dias.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.330100?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    3. Kharin, S., 2018. "Price Transmission Analysis: the Case of Milk Products in Russia," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(01).
    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).
    5. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    6. Ayinde, Opeyemi Eyitayo & Ilori, T. E. & Ayinde, K. & 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, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 7(4), pages 1-15, December.
    7. Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Awe, Olushina Olawale & Musa, Ann Precious & Sanusi, Gbenga Peter, 2023. "Revisiting economic diversification in Africa's largest resource-rich nation: Empirical insights from unsupervised machine learning," Resources Policy, Elsevier, vol. 82(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Awe, Olushina Olawale & Musa, Ann Precious & Sanusi, Gbenga Peter, 2023. "Revisiting economic diversification in Africa's largest resource-rich nation: Empirical insights from unsupervised machine learning," Resources Policy, Elsevier, vol. 82(C).
    2. 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).
    3. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Risks, MDPI, vol. 8(3), pages 1-20, September.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    7. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    8. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.
    9. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    10. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    11. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    12. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    13. Georgia Koppe & Hazem Toutounji & Peter Kirsch & Stefanie Lis & Daniel Durstewitz, 2019. "Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-35, August.
    14. Steven Y. K. Wong & Jennifer Chan & Lamiae Azizi & Richard Y. D. Xu, 2020. "Time-varying neural network for stock return prediction," Papers 2003.02515, arXiv.org, revised Jan 2021.
    15. Arthur Charpentier & Molly James & Hani Ali, 2021. "Predicting Drought and Subsidence Risks in France," Papers 2107.07668, arXiv.org.
    16. Yang Sheng & Weizhong Liu & Hailiang Xu & Xianchao Gao, 2021. "The Spatial Distribution Characteristics of the Cultivated Land Quality in the Diluvial Fan Terrain of the Arid Region: A Case Study of Jimsar County, Xinjiang, China," Land, MDPI, vol. 10(9), pages 1-29, August.
    17. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    18. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    19. Alper Taner & Yeşim Benal Öztekin & Hüseyin Duran, 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
    20. Li, Baibing, 2022. "Stochastic modeling and adaptive forecasting for parking space availability with drivers’ time-varying arrival/departure behavior," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 313-332.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aolpei:330100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/fevszcz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.