SHS Web Conf.
Volume 71, 2019Eurasia: Sustainable Development, Security, Cooperation – 2019
|Number of page(s)||8|
|Section||New Geopolitical Trends in Central Eurasia|
|Published online||25 November 2019|
Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations
Institute of Technology and Business, School of Expertness and Valuation, České Budějovice, Czech Republic
* Corresponding author: firstname.lastname@example.org.
The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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