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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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