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Global Multidisciplinary Journal

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Cloud Deployed Ensemble Deep Learning Architectures for Predictive Modeling of Cryptocurrency Market Dynamics

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Dr. Michael R. Hoffman 1 ,
4 Department of Computer Science, University of Zurich, Switzerland

Abstract

Cryptocurrency markets have emerged as one of the most complex, volatile, and information intensive financial ecosystems of the digital economy. Unlike traditional equity or commodity markets, crypto assets are characterized by twenty four hour global trading, extreme price sensitivity to socio technical signals, fragmented liquidity, and algorithmic dominance in trading execution. These features collectively create a nonlinear, high dimensional, and dynamically evolving data environment that challenges classical econometric and statistical forecasting models. The rise of deep learning and ensemble based artificial intelligence has therefore been accompanied by growing academic and industrial interest in their application to cryptocurrency trend prediction, particularly when deployed on scalable cloud infrastructures that can process continuous data streams in real time.

Methodologically, the paper presents a text based but highly detailed design of a cloud deployed ensemble deep learning system for crypto trend modeling. It elaborates on data acquisition, feature engineering, model heterogeneity, ensemble fusion strategies, and cloud orchestration mechanisms, while critically discussing their limitations and trade offs. The results section provides a rich interpretive account of how ensemble deep learning improves robustness, reduces variance, and adapts to regime shifts in cryptocurrency markets, grounding these claims in the existing literature. The discussion extends these findings through theoretical synthesis, critical debate, and exploration of future research directions, including uncertainty quantification, meta learning, and decentralized deployment paradigms.

By offering a deeply elaborated, literature grounded, and theoretically integrated account of cloud deployed ensemble deep learning for cryptocurrency trend prediction, this article contributes to both academic scholarship and applied financial technology. It demonstrates that the convergence of ensemble intelligence and cloud computing represents not merely a technical upgrade, but a paradigmatic shift in how volatile, data intensive financial systems can be modeled, understood, and forecasted in the digital age.

 

Keywords

Cryptocurrency forecasting, ensemble deep learning, cloud computing, financial time series

References

📄 Khamparia, A., Singh, A., Anand, D., Gupta, D., Khanna, A., Arun Kumar, N., Tan, J. (2020). A novel deep learning based multi model ensemble method for the prediction of neuromuscular disorders. Neural Computation and Applications 32.
📄 Hassan, A., Abbasi, A., Zeng, D. (2013). Twitter sentiment analysis: A bootstrap ensemble framework. 2013 International Conference on Social Computing. IEEE.
📄 Kanikanti, V. S. N., Nagavalli, S. P., Varanasi, S. R., Sresth, V., Gandhi, A., Lakhina, U. (2025). Predictive Modeling of Crypto Currency Trends Using Cloud Deployed Ensemble Deep Learning. Proceedings of the 2025 IEEE International Conference on Computing.
📄 Lakshminarayanan, B., Pritzel, A., Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems 30.
📄 Araque, O., Corcuera Platas, I., Sanchez Rada, J. F., Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications.
📄 Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems.
📄 Huang, S., Wang, B., Qiu, J., Yao, J., Wang, G., Yu, G. (2016). Parallel ensemble of online sequential extreme learning machine based on mapreduce. Neurocomputing.
📄 Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media news. Journal of Ambient Intelligence and Humanized Computing.
📄 Bauer, E., Kohavi, R. (1999). Empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning.
📄 Guo, P., Xue, Z., Mtema, Z., Yeates, K., Ginsburg, O., Demarco, M., Antani, S. (2020). Ensemble deep learning for cervix image selection toward improving reliability in automated cervical precancer screening. Diagnostics.
📄 Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A. (2021). Meta learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence.
📄 Haque, A., Milstein, A., Fei Fei, L. (2020). Illuminating the dark spaces of healthcare with ambient intelligence. Nature.
📄 Hormozi, E., Akbari, M. K., Hormozi, H., Javan, M. S. (2013). Accuracy evaluation of a credit card fraud detection system on Hadoop MapReduce. IEEE Conference on Information and Knowledge Technology.
📄 Kumar, G., Misra, A. K. (2018). Commonality in liquidity: Evidence from Indias national stock exchange. Journal of Asian Economics.
📄 Alam, M., Samad, M. D., Vidyaratne, L., et al. (2020). Survey on deep neural networks in speech and vision systems. Neurocomputing.
📄 Hashino, T., Bradley, A., Schwartz, S. (2007). Evaluation of bias correction methods for ensemble streamflow volume forecasts. Hydrology and Earth System Sciences.
📄 Kim, H. C., Pang, S., Je, H. M., Kim, D., Bang, S. Y. (2003). Constructing support vector machine ensemble. Pattern Recognition.
📄 Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion.
📄 Kermany, D. S., Goldbaum, M., Cai, W., et al. (2018). Identifying medical diagnoses and treatable diseases by image based deep learning. Cell.
📄 Heikal, M., Torki, M., El Makky, N. (2018). Sentiment analysis of arabic tweets using deep learning. Procedia Computer Science.
📄 Koh, N. S., Hu, N., Clemons, E. K. (2010). Do online reviews reflect a products true perceived quality. Electronic Commerce Research and Applications.

How to Cite

Dr. Michael R. Hoffman. (2026). Cloud Deployed Ensemble Deep Learning Architectures for Predictive Modeling of Cryptocurrency Market Dynamics. Global Multidisciplinary Journal, 5(01), 92-102. https://www.grpublishing.org/journals/index.php/gmj/article/view/318

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