DEVELOPMENT OF STRATEGY FOR ETHEREUM PRICE ANALYSIS USING DEEP LEARNING BASED ON TIME SERIES DATA
Abstract
The economic era is changing quickly throughout the world. Looking at the history of the currencies, from the barter system to Bitcoin and now to Ethereum, the core aim is to predict the cost. Ethereum differs from Bitcoin in many aspects, like hashing algorithms and the execution time of blocks. However, as Ethereum is a programmable code taking part in transactions considering smart contracts, it is necessary to predict the Ethereum prize based on historical and live data. Hence, this paper presents the proposed algorithm ‘DeepCoinCap,’ which can compare the various datasets using deep learning training and validation of time-series datasets. The core aim is to identify the Ethereum price trends and correlated elements which impact the future cost. The aspects like social situations, economic changes, and geographical conditions can affect the Ethereum price trends, which need to be validated.
Keyword : Block chain, deep learning, machine learning, price prediction, Ethereum
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1] Jagannath, N., Barbulescu, T., Sallam, K. M., Elgendi, I., McGrath, B., Jamalipour, A. & Munasinghe, K. (2021). An on-chain analysis-based approach to predict ethereum prices. IEEE Access, 9, 167972-167989. [2] de Azevedo Sousa, J. E., Oliveira, V., Valadares, J., Dias Goncalves, G., Moraes Villela, S., Soares Bernardino, H., & Borges Vieira, A. (2021). An analysis of the fees and pending time correlation in Ethereum. International Journal of Network Management, 31(3), e2113. [3] Zhao, L., Sen Gupta, S., Khan, A., & Luo, R. (2021, April). Temporal analysis of the entire ethereum blockchain network. In Proceedings of the Web Conference 2021 (pp. 2258-2269). [4] Politis, A., Doka, K., & Koziris, N. (2021, May). Ether price prediction using advanced deep learning models. In 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 1-3). IEEE. [5] Alden, L. An Economic Analysis of Ethereum. Lyn Alden Investment Strategy https://www. lynalden. com/ethereum-analysis. [6] Dash, A. B., Mishra, B., & Singh, A. N. (2021). Identification of Premature Diagnosis for Detection of Brain Tumor Using Blockchain Strategy. In Next Generation of Internet of Things: Proceedings of ICNGIoT 2021 (pp. 31-38). Springer Singapore. [7] Gupta, S., & Gupta, M. (2021, October). Deep learning for brain tumor segmentation using magnetic resonance images. In 2021 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB) (pp. 1-6). IEEE. [8] Kumar, D., & Rath, S. K. (2020). Predicting the trends of price for ethereum using deep learning techniques. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 103-114). Springer Singapore. [9] Derbentsev, V., Matviychuk, A., & Soloviev, V. N. (2020). Forecasting of cryptocurrency prices using machine learning. Advanced Studies of Financial Technologies and Cryptocurrency Markets, 211-231. [10] Awoke, T., Rout, M., Mohanty, L., & Satapathy, S. C. (2020). Bitcoin price prediction and analysis using deep learning models. In Communication Software and Networks: Proceedings of INDIA 2019 (pp. 631-640). Singapore: Springer Singapore. [11] Bai, C., White, T., Xiao, L., Subrahmanian, V. S., & Zhou, Z. (2019, July). C2P2: a collective cryptocurrency up/down price prediction engine. In 2019 IEEE International Conference on Blockchain (Blockchain) (pp. 425-430). IEEE. [12] Altan, A., Karasu, S., & Bekiros, S. (2019). Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos, Solitons & Fractals, Elsevier, 126, 325-336. [13] Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898. [14] Li, T. R., Chamrajnagar, A. S., Fong, X. R., Rizik, N. R., & Fu, F. (2019). Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Frontiers in Physics, 7, 98.