Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs

Το τεκμήριο παρέχεται από τον φορέα :
University of Patras   

Αποθετήριο :
Nemertes   

δείτε την πρωτότυπη σελίδα τεκμηρίου
στον ιστότοπο του αποθετηρίου του φορέα για περισσότερες πληροφορίες και για να δείτε όλα τα ψηφιακά αρχεία του τεκμηρίου*



Ερευνητικά ερωτήματα και προτάσεις για τη πρόβλεψη κρυπτονομισμάτων χρησιμοποιώντας βαθιά νευρωνικά δίκτυα (EL)
Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs (EL)

Livieris, Ioannis
Kotsilieris, Theodore
Pintelas, Emmanuel
Stavroyiannis, Stavros
Pintelas, Panagiotis

Πιντέλας, Παναγιώτης
Κοτσιλιέρης, Θεόδωρος
Πιντέλας, Εμμανουήλ
Σταυρόγιαννης, Σταύρος
Λιβιέρης, Ιωάννης

Technical Report (EL)

2020-02-21
2020-03-09T10:27:54Z


In last decade, cryptocurrency has emerged in financial area as a key factor in businesses and financial market opportunities. Accurate predictions can assist cryptocurrency investors towards right investing decisions and lead to potential increased profits. Additionally, they can also support policy makers and financial researchers in studying cryptocurrency markets behavior. Nevertheless, cryptocurrency price prediction is considered a very challenging task, due to its chaotic and very complex nature. In this study we investigate three major research questions: i) Can deep learning efficiently predict cryptocurrency prices? ii) Are cryptocurrency prices a random walk process? iii) Is there a proper validation method of cryptocurrency price prediction models? To this end, we evaluate some of the most successful and widely used in bibliography deep learning algorithms forecasting cryptocurrency prices. The results obtained, provide significant evidence that deep learning models are not able to solve this problem efficiently and effectively. Following detailed experimentation and results analysis, we conclude that it is essential to invent and incorporate new techniques, strategies and alternative approaches such as more sophisticated prediction algorithms, advanced ensemble methods, feature engineering techniques and other validation metrics. (EL)


Deep learning (EL)
CNN (EL)
LSTM (EL)
Βαθιά μάθηση (EL)
Time-series (EL)
BiLSTM (EL)
Cryptocurrency price prediction (EL)
Χρονοσειρές (EL)





*Η εύρυθμη και αδιάλειπτη λειτουργία των διαδικτυακών διευθύνσεων των συλλογών (ψηφιακό αρχείο, καρτέλα τεκμηρίου στο αποθετήριο) είναι αποκλειστική ευθύνη των αντίστοιχων Φορέων περιεχομένου.