Version 1
: Received: 22 October 2024 / Approved: 23 October 2024 / Online: 23 October 2024 (14:48:35 CEST)
How to cite:
Lee, M. C. Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators. Preprints2024, 2024101852. https://doi.org/10.20944/preprints202410.1852.v1
Lee, M. C. Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators. Preprints 2024, 2024101852. https://doi.org/10.20944/preprints202410.1852.v1
Lee, M. C. Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators. Preprints2024, 2024101852. https://doi.org/10.20944/preprints202410.1852.v1
APA Style
Lee, M. C. (2024). Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators. Preprints. https://doi.org/10.20944/preprints202410.1852.v1
Chicago/Turabian Style
Lee, M. C. 2024 "Attention-Based Deep Learning Models for Cryptocurrency Price Prediction: A Comparative Analysis with Technical Indicators" Preprints. https://doi.org/10.20944/preprints202410.1852.v1
Abstract
This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting cryptocurrency price movements. The models utilize historical OHLCV data, combined with four technical indicators: SMA, EMA, TEMA, and MACD, to enhance the accuracy of classification into three categories: uptrend, downtrend, and neutral. Both models aim to capture market dynamics through sequential data while incorporating attention mechanisms to focus on relevant time steps. Experimental results demonstrate that the inclusion of technical indicators significantly improves model performance, with MACD yielding the highest accuracy. The Attention-GRU model shows computational advantages, while the Attention-LSTM model excels in capturing long-term dependencies.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
