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Titel: Machine Learning-Based Forecasting of Bitcoin Price Movements
Autor(en): Angelovski, Darko
Velichkovska, Bojana
Jakimovski, Goran
Efnusheva, Danijela
Kalendar, MarijaIn der Gemeinsamen Normdatei der DNB nachschlagen
Körperschaft: Hochschule Anhalt
Erscheinungsdatum: 2024
Sprache: Englisch
Schlagwörter: Datenverarbeitung
Informationstechnik
Zusammenfassung: In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks, Gradient Boosting, LightGBM (LGBM) Regressor, and Random Forest algorithms to unravel the complexities of price movements. We perform a comprehensive analysis, and observe patterns and dependencies within historical data at hour-long intervals in the last 30 and 45 days, by using a holdout technique with 80% of the data used for training and 20% used for testing. We evaluate the models using four standard regression metrics. The training data incorporates a diverse range of features capturing hourly trends, day-of-the-week variations, and the correlation between opening and closing prices. Our study delves into the ability for forecasting Bitcoin price movements using ensemble algorithms and LSTM. The results show best performance for the LSTM models, especially when trained on longer training intervals. Namely, our LSTM model obtains R2 of 0.98 when trained on 30 days and 0.99 when trained on 45 days. In comparison, the ensemble methods show volatility and lower predictive ability.
URI: https://opendata.uni-halle.de//handle/1981185920/117598
http://dx.doi.org/10.25673/115643
Open-Access: Open-Access-Publikation
Nutzungslizenz: (CC BY-SA 4.0) Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International(CC BY-SA 4.0) Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International
Enthalten in den Sammlungen:International Conference on Applied Innovations in IT (ICAIIT)

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