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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

Botunac, Ive ; Panjkota, Ante ; Matetić, Maja The importance of time series data filtering for predicting the direction of stock market movement using neural networks // Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium .... 2019. str. 886-891 doi: 10.2507/30th.daaam.proceedings.123

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

Poveznice
Indeksiranost