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AI 2021, 2 494 References Table 5 shows a comparison between the proposed model in this paper and other models in the literature [45,51,53]. The MAPES values of the proposed model in this paper for GRU predicting LTC represents the best performance compared to all other models as the predicted results are very close to the actual results. Results obtained from this paper show that the GRU performed better when predicting the price of all types of cryptocurrency than the LSTM and the bi-LSTM models. 7. Conclusions In this paper, three types of machine learning algorithm are constructed and used for predicting the prices of three types of cryptocurrency—BTC, ETH, and LTC. Performance measures were conducted to test the accuracy of different models as shown in Tables 2–4. Then, we compared the actual and predicted prices. The results show that GRU outper- formed the other algorithms with a MAPE of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The RMSE for the GRU model was found to be 174.129, 26.59, and 0.825 for BTC, ETH, and LTC, respectively. Based on these outcomes, the GRU model for the targeted cryptocurrencies can be considered efficient and reliable. This model is considered the best model. However, bi-LSTM represents less accuracy than GRU and LSTM with substantial differences between the actual and the predicted prices for both BTC and ETH. The experimental results show that: • The AI algorithm is reliable and acceptable for cryptocurrency prediction. • GRU can predict cryptocurrency prices better than LSTM and bi-LSTM but overall all algorithms represent excellent predictive results. In future work, we will investigate other factors that might affect the prices of the cryptocurrency market, and we will focus on the effect that social media in general and tweets in particular can have on the price and trading volume of cryptocurrencies by analyzing tweets using natural language processing techniques and sentiment analysis. Author Contributions: M.J.H. designed and built the models, processed the data, and wrote the first draft of the manuscript; A.Y.O. helped in the processing of the data and in the interpretation of the results. A.Y.O. revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research is funded by the Arab American University in Palestine, and the APC was funded by MDPI AI. Data Availability Statement: Data used for this article is publicly available and collected from https://www.marketwatch.com. Conflicts of Interest: The authors declare no conflict of interest. 1. Mukhopadhyay, U.; Skjellum, A.; Hambolu, O.; Oakley, J.; Yu, L.; Brooks, R. A brief survey of Cryptocurrency systems. In Proceedings of the 14th Annual Conference on Privacy, Security and Trust (PST), Auckland, New Zealand, 12–14 December 2016; pp. 745–752. [CrossRef] 2. Rose, C. The Evolution Of Digital Currencies: Bitcoin, A Cryptocurrency Causing A Monetary Revolution. Int. Bus. Econ. Res. J. (IBER) 2015, 14, 617. [CrossRef] 3. Brenig, C.; Accorsi, R.; Müller, G. Economic Analysis of Cryptocurrency Backed Money Laundering. ECIS 2015 Completed Research Papers. Paper 20. 2015. Available online: https://aisel.aisnet.org/ecis2015_cr/20 (accessed on 16 June 2021). 4. Eyal, I. Blockchain Technology: Transforming Libertarian Cryptocurrency Dreams to Finance and Banking Realities. Computer 2017, 50, 38–49. [CrossRef] 5. DeVries, P. An Analysis of Cryptocurrency, Bitcoin, and the Future. Int. J. Bus. Manag. Commer. 2016, 1, 1–9. 6. Jang, H.; Lee, J. An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information. IEEE Access 2017, 6, 5427–5437. [CrossRef] 7. Saad, M.; Choi, J.; Nyang, D.; Kim, J.; Mohaisen, A. Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions. IEEE Syst. J. 2019, 14, 321–332. [CrossRef] 8. Gautam, K.; Sharma, N.; Kumar, P. Empirical Analysis of Current Cryptocurrencies in Different Aspects. In Proceedings of the ICRITO 2020—IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), Noida, India, 4–5 June 2020; pp. 344–348. [CrossRef]PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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