Cryptocurrency Price Prediction using a Hybrid Deep Learning Approach with Explainable AI Integration

Liu, Chaolu, Rustam, Furqan and Jurcut, Anca Delia (2026) Cryptocurrency Price Prediction using a Hybrid Deep Learning Approach with Explainable AI Integration. In: Proceedings of the 2025 9th International Conference on Computer Science and Artificial Intelligence. CSAI (2025). ACM, Beijing, pp. 637-642. ISBN 979-840071962-2

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Abstract

Cryptocurrency markets exhibit pronounced nonlinearity and abrupt regime shifts, making accurate price forecasting a formidable challenge. Conventional econometric models such as ARIMA and GARCH often fail to capture these complex dynamics under high-volatility conditions. In response, we propose a hybrid deep learning framework, a ConvLSTM-GRU pipeline, that combines one-dimensional convolutions for localized pattern extraction. LSTM layers for long-term dependency modelling, and GRU layers to selectively refine salient temporal features. Technical indicators (e.g., moving averages, Bollinger Bands, and RSI) and trading volume are integrated as input channels to enrich the feature space with signals that capture volatility and momentum. We then employ Keras Tuner's Hyperband algorithm to search over convolutional filter counts, recurrent unit sizes, dense layer widths, dropout rates, and learning rates, optimizing the network for minimal validation mean squared error. On a chronological split of BTCUSDT daily closing prices from August 2017 to July 2025, the tuned ConvLSTM-GRU achieves an R2 of 0.9922 on the held-out test set, outperforming standalone LSTM, GRU, CNN, and traditional machine learning baselines. Furthermore, we applied SHAP with ConvLSTM-GRU to improve decision-making transparency and trustworthiness.

Item Type:	Book Section
Uncontrolled Keywords:	ConvLSTM-GRU; Cryptocurrency Forecasting; Deep Learning; Explainable AI; Technical Analysis
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	28 Apr 2026 15:15
Last Modified:	28 Apr 2026 15:15
URI:	https://norma.ncirl.ie/id/eprint/9292

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