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Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China

Author

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  • Petr Suler

    (School of Expertness and Valuation, Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceske Budejovice, Czech Republic)

  • Zuzana Rowland

    (School of Expertness and Valuation, Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceske Budejovice, Czech Republic)

  • Tomas Krulicky

    (School of Expertness and Valuation, Institute of Technology and Business in České Budějovice, Okružní 517/10, 370 01 Ceske Budejovice, Czech Republic)

Abstract

The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC.

Suggested Citation

  • Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:2:p:76-:d:496972
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    1. Wu, Gang & Liu, Lan-Cui & Wei, Yi-Ming, 2009. "Comparison of China's oil import risk: Results based on portfolio theory and a diversification index approach," Energy Policy, Elsevier, vol. 37(9), pages 3557-3565, September.
    2. Kedong Yin & Danning Lu & Xuemei Li, 2017. "A Novel Grey Wave Method for Predicting Total Chinese Trade Volume," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
    3. Chongyu Wu & Jiantao Zhou & Hui Li & Zhongyi Liu & Xinyi Cai, 2017. "Do Imported Commodities Cause Inflation in China? An Armington Substitution Elasticity Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(2), pages 400-415, February.
    4. Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
    5. Hongwei Liao & Liangping Yang & Henan Ma & JiaoJiao Zheng, 2020. "Technology import, secondary innovation, and industrial structure optimization: A potential innovation strategy for China," Pacific Economic Review, Wiley Blackwell, vol. 25(2), pages 145-160, May.
    6. Karel Janda & Eva Michalikova & Jiri Skuhrovec, 2013. "Credit Support for Export: Robust Evidence from the Czech Republic," The World Economy, Wiley Blackwell, vol. 36(12), pages 1588-1610, December.
    7. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    8. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    9. Karel Janda & Eva Michalikova & Jiri Skuhrovec, 2012. "Credit Support for Export: Evidence from the Czech Republic," CERGE-EI Working Papers wp461, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    11. David Havrlant & Roman Hušek, 2011. "Models of Factors Driving the Czech Export," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(3), pages 195-215.
    12. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    13. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    14. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    15. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    16. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    17. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    18. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    19. Allen, Creina & Day, Garth, 2014. "Depletion of non-renewable resources imported by China," China Economic Review, Elsevier, vol. 30(C), pages 235-243.
    20. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
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