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master's thesis
Neuronske mreže i predikcija cijena dionica
2024. urn:nbn:hr:126:555433
Žuro, Matko
Josip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics

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Žuro, M. (2024). Neuronske mreže i predikcija cijena dionica (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:555433

Žuro, Matko. "Neuronske mreže i predikcija cijena dionica." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:555433

Žuro, Matko. "Neuronske mreže i predikcija cijena dionica." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:555433

Žuro, M. (2024). 'Neuronske mreže i predikcija cijena dionica', Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, accessed 27 October 2024, https://urn.nsk.hr/urn:nbn:hr:126:555433

Žuro M. Neuronske mreže i predikcija cijena dionica [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics; 2024 [cited 2024 October 27] Available at: https://urn.nsk.hr/urn:nbn:hr:126:555433

M. Žuro, "Neuronske mreže i predikcija cijena dionica", Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:126:555433

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Metadata
TitleNeuronske mreže i predikcija cijena dionica
Title (english)Neural Networks And Stock Price Prediction
AuthorMatko Žuro
MentorDanijel Grahovac (mentor)
Committee memberNenad Šuvak (predsjednik povjerenstva)
Committee memberDanijel Grahovac (član povjerenstva)
Committee memberIvan Papić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics
Osijek
Defense date and country2024-10-10, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Applied Mathematics and Mathematical Modeling
Abstract
Ovaj rad istražuje primjenu Long Short-Term Memory (LSTM) mreža za predikciju cijena dionica. Analizom povijesnih podataka AAPL dionica, model pokazuje značajan potencijal u unapređenju točnosti predikcija i uspješnosti strategije trgovanja. Rad se fokusira na ključne komponente neuronskih mreža, proces treniranja i izazove poput prenaučenosti. Rezultati sugeriraju da LSTM mreže mogu pružiti vrijedne uvide za razvoj investicijskih strategija.
Abstract (english)
This paper explores the application of Long Short-Term Memory (LSTM) networks for stock price prediction. Through the analysis of historical AAPL stock data, the model demonstrates significant potential in improving prediction accuracy and trading strategy performance. The work focuses on key components of neural networks, the training process, and challenges such as overfitting. The results suggest that LSTM networks can provide valuable insights for the development of investment strategies.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:555433
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Financial and Statistical Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-25 10:00:07