Forecasting Dow Jones Industrial Average Index Movement Using Long Short-Term Memory network (CROSBI ID 699391)
Prilog sa skupa u zborniku | kratko priopćenje
Podaci o odgovornosti
Štifanic, Daniel ; Jukić, Adrijana ; Musulin, Jelena ; Car, Zlatan
engleski
Forecasting Dow Jones Industrial Average Index Movement Using Long Short-Term Memory network
The stock market tends to be a noisy, non- stationary and non-linear dynamical system, therefore, forecasting stock price movement is quite difficult. Numerous factors interact in finance including economic conditions, COVID-19 pandemic, political events, etc. However, with the help of artificial intelligence-based system, supportive and valuable information can be provided to the traders and analysts, which may be crucial for successful investment and maximizing profits. The approach that provided satisfactory results in terms of stock market forecasting is an integration of stationary wavelet transform (SWT) with long short-term memory networks (LSTMs).
stationary wavelet transform (SWT) ; long short-term memory networks (LSTMs) ; COVID-19 ; Forecasting Dow Jones
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Podaci o prilogu
45-46.
2020.
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objavljeno
Podaci o matičnoj publikaciji
Abstract Book - Fifth International Workshop on Data Science
Lončarić, Sven
Zagreb: Znanstveni centar izvrsnosti za znanost o podatcima i kooperativne sustave
Podaci o skupu
5th International Workshop on Data Science (IWDS 2020)
predavanje
24.11.2020-24.11.2020
Zagreb, Hrvatska