The importance of time series data filtering for predicting the direction of stock market movement using neural networks (CROSBI ID 682775)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Botunac, Ive ; Panjkota, Ante ; Matetić, Maja
engleski
The importance of time series data filtering for predicting the direction of stock market movement using neural networks
Predicting future trends in the stock market from time-series data is a challenging task due to its high non-linear nature caused by the complexity involved in the trading process. This paper emphasizes the importance of time- series dana filtering when neural network models are used for stock market direction forecasting. Performances of three different neural network models are compared on raw data, processed data with simple moving average, and data filtered with discrete wavelet transformation. Applying wavelet transformation on input financial data as a processing step shows better results than the use ofraw financial data or simple moving average. Also, among tested neural network models, the better results are obtained by using long short-term neural network then by using other neural network models.
stock market prediction ; machine learning ; neural network ; wavelet transformation
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Podaci o prilogu
886-891.
2019.
objavljeno
10.2507/30th.daaam.proceedings.123
Podaci o matičnoj publikaciji
Proceedings of the 30th International DAAAM Symposium ''Intelligent Manufacturing & Automation''
978-3-902734-22-8
1726-9679
Podaci o skupu
30th DAAAM International Symposium on Intelligent Manufacturing and Automation
poster
23.10.2019-26.10.2019
Zadar, Hrvatska
Povezanost rada
Ekonomija, Informacijske i komunikacijske znanosti, Računarstvo