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

Marek Vochozka, Jakub Horák and Petr Šuleř
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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

JRFM, 2019, vol. 12, issue 2, 1-17

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.

Keywords: exchange rate; artificial neural networks; prediction; equalizing time series; seasonal fluctuations; categorical variable (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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