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Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate

Author

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  • Marek Vochozka

    (The Institute of Technology and Business in České Budějovice, Okružní 517/10, 37001 České Budějovice, Czech Republic)

  • Jakub Horák

    (The Institute of Technology and Business in České Budějovice, Okružní 517/10, 37001 České Budějovice, Czech Republic)

  • Petr Šuleř

    (The Institute of Technology and Business in České Budějovice, Okružní 517/10, 37001 České Budějovice, Czech Republic)

Abstract

The exchange rate is one of the most monitored economic variables reflecting the state of the economy in the long run, while affecting it significantly in the short run. However, prediction of the exchange rate is very complicated. In this contribution, for the purposes of predicting the exchange rate, artificial neural networks are used, which have brought quality and valuable results in a number of research programs. This contribution aims to propose a methodology for considering seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of Euro and Chinese Yuan. For the analysis, data on the exchange rate of these currencies per period longer than 9 years are used (3303 input data in total). Regression by means of neural networks is carried out. There are two network sets generated, of which the second one focuses on the seasonal fluctuations. Before the experiment, it had seemed that there was no reason to include categorical variables in the calculation. The result, however, indicated that additional variables in the form of year, month, day in the month, and day in the week, in which the value was measured, have brought higher accuracy and order in equalizing of the time series.

Suggested Citation

  • Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:76-:d:227157
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    References listed on IDEAS

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