Assessing machine learning performance in cryptocurrency market price prediction

[1] Patel, M.M., Tanwar, S., Grupta, R., Kumar, N., 2020, A deep learning-based cryptocurrency price prediction scheme for  nancial institutions,Journal of Information Security and Applications, 55, 102-583.
[2] Technical Indicators and Overlays (ChartSchool), November 2018,
[3] Kritzer, A., Service, S.O., 2012, Forex for beginners [electronic resource]: a comprehensive guide to profting from the global currency markets/by Adam Kritzer.
[4] Archer, M.D., 2010, Getting started in currency trading: winning in todays Forex market. Wiley, London, p 333 Bahrammirzaee A (2010) A comparative survey of artifcial intelligence applications in fnance: artifcial neural networks, expert system and hybrid intelligent systems,Neural Comput Appl, 19, 11651195.
[5] Murphy, J.J., 1999, Technical analysis of the fnancial markets: TA - Book, 239.
[6] Ozorhan, M.O., Toroslu, I.H., Sehitoglu, O.T., 2017, A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms, Soft Comput,21, 66536671.
[7] Huang, J., Chai, J., Cho, S., 2020,Deep learning in  nance and banking: A literaturereview and classi cation, Frontiers of Business Research in China,14, 1-24.
[8] Sethia, A., Raut. P., 2019, Application of LSTM, GRU and ICA for stock price prediction, Information and Communication Technology for Intelligent Systems, 479-487.
[9] Fischer, T., Krauss, Ch., 2018, Deep learning with long short-term memory networks fornancial market predictions European Journal of Operational Research, 2(270), 654-669.
[10] Ghosh, A., Bose, S., Maji, G., Debnath, N., Sen, S., 2019, Stock price prediction using LSTM on Indian Share MarketProceedings of 32nd international conference on, 40, 101-110.
[11] Gite, Sh., Khatavkar, H., Srivastava, Sh., Maheshwari, P., Pandey, N., 2021, Stock Prices Prediction from Financial News Articles Using LSTM and XAI, Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 153-161.
[12] Qiu, J., Wang, B., Zhou, Ch., 2020, Forecasting stock prices with long-short term memory neural network based on attention mechanism, Public Library of Science San Francisco, CA USA, 15(1), 222-227.
[13] Fadda, S., 2020, Pricing options with dual volatility input to modular neural networks, Borsa Istanbul Review, 20(3) 269-278.
[14] Aggarwal, A., Gupta I., Garg, N., Goel A., 2019, Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction, Twelfth International Conference on Contemporary Computing (IC3)IEEE, 15.
[15] Nakamoto, S., 2008, Bitcoin: A peer-to-peer electronic cash system, Decentralized Business Review.
[16] Sovbetov, Y., 2018 Factors in uencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero, Journal of Economics and Financial Analysis, 2(2) 1-27.
[17] Teker, D., Teker, S., Ozyesil, M, 2019, Determinants of cryptocurrency price movements, 14th Paris international conference on marketing, economics, education and inter-disciplinary studies, MEEIS-19, 2(2) 12-14.
[18] Li, G., Zhao, Q., Song, M., Du, D., Yuan, J., Chen, X., Liang, H., 2019, Predicting global computing power of blockchain using cryptocurrency prices, 2019 International Conference on Machine Learning and Cybernetics (ICMLC), 2(2) 1-6.

[19] Peng, Y., Albuquerque, P.H.M., Sa, J.M.C, Padula, A.J.A, Montenegro, M.R., 2018, The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression, Expert Systems with Applications, 97,177-192.
[20] Poongodi, M., Sharma, A., Vijayakumar, V., Bhardwaj, V., Sharma, A.P., Iqbal, R., Kumar, R., 2020, Prediction of the price of Ethereum blockchain cryptocurrency in an industrial  nance system, Computers & Electrical Engineering, 81, 106-527.
[21] Sin, E., Wang, L., 2017, Bitcoin price prediction using ensembles of neural networks, 2017 13th International conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), 666-671.
[22] Jain, A., Tripathi, Sh., Dwivedi, H.D., Saxena, P., 2018, Forecasting price of cryptocurrencies using tweets sentiment analysis, 2018 eleventh international conference on contemporary computing (IC3), 1-7.
[23] Madanayake, A., Wimalagunaratne, R., Dansereau, D., Bruton, L.T., 2011, Design and FPGA-implementation of 1 st order 4D IIR frequency-hyperplanar digital  lters, 2011 IEEE 54th international midwest symposium on circuits and systems (MWSCAS), 1-4.
[24] Smuts, N., 2019, What drives cryptocurrency prices? An investigation of google trends and telegram sentiment, ACM SIGMETRICS Performance Evaluation Review, 46(3), 131-134.
[25] Cheuque, C., German, L., Reutter, J., 2019, Bitcoin price prediction through opinion mining, Companion Proceedings of The 2019 World Wide Web Conference, 755{762.
[26] Mittal, A., Dhiman, V., Singh, A., Prakash, Ch., 2019, Short-term bitcoin price  uctuation prediction using social media and web search data, 2019 Twelfth International Conference on Contemporary Computing (IC3), 1-6.
[27] Mohanty, P., Patel, D., Patel, P., Roy, S., 2018, Predicting  uctuations in cryptocurrencies' price using users' comments and real-time prices, 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 477-482.
[28] Selvamuthu, D., Kumar, V., Mishra, A., 2019, Indian stock market prediction using articial neural networks on tick data, Financial Innovation,5(1), 1-12.
[29] Mallqui, D., Fernandes, R., 2019, Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques, Applied Soft Computing, 75, 596-606.
[30] Adebiyi, A., Ayo, Ch., Adebiyi, M., Otokiti, S., 2012, Stock price prediction using neural network with hybridized market indicators, Journal of Emerging Trends in Computing and Information Sciences, 3(1).
[31] Lippmann, R., 1987, An introduction to computing with neural nets, IEEE Assp magazine, 4(2), 4-22.
[32] Dutta, S., Shekhar, Sh., 1988, Bond rating: a non-conservative application of neural networks, IEEE Int Conf on Neural Networks, 443-450.
[33] Poria, S., Cambria, E., Gelbukh, A., 2016, Aspect extraction for opinion mining with a deep convolutional neural network, Knowledge-Based Systems, 108, 42-49.
[34] LeCun, Y., Bengio, Y., Hinton, G., 2015, Deep learning, Nature Publishing Group, 521,436-444.
[35] Schmidhuber, L., 2015, Deep learning in neural networks: An overview, Neural networks, 61, 85-115.
[36] Kucharcikova, A., Konusikova, L., Tokarcikova, E., 2016, Approaches to the quantication of the human capital efficiency in enterprises, Communications-Scienti c letters of the University of Zilina, 18(1A), 49-54.
[37] Pimentel, H., Bray, N., Puente, S., Melsted, S., Pachter, L., 2017, Differential analysis of RNA-seq incorporating quanti cation uncertainty, Nature methods, 14(7), 687-690.
[38] Arora, N., et. al., 2019, Financial analysis: stock market prediction using deep learning algorithms, Proceedings of International Conference on Sustainable Computing in Science,mTechnology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India.
[39] Naik, N., Mohan, B.R., 2019, Stock price movements classi cation using machine and deep learning techniques-the case study of indian stock market, International Conference on Engineering Applications of Neural Networks, 445-452.
[40] Sak, H., Senior, A., Beaufays, F., 2014, Long short-term memory recurrent neural network architectures for large scale acoustic modeling.

[41] Wu, Ch., Lu, Ch., Ma, Yu., Lu, Ru., 2018, A new forecasting framework for bitcoin price with LSTM, 2018 IEEE International Conference on Data Mining Workshops (ICDMW),168-175.
[42] Yang, Sh., Yu, Xu., Zhou, Y., 2020, LSTM and GRU neural network performance comparison study: Taking Yelp review dataset as an example, 2020 International workshop on electronic communication and arti cial intelligence (IWECAI), 98-101.
[43] Tandon, S., Tripathi, Sh., Saraswat, P., Dabas, Ch., 2019, Bitcoin price forecasting using  lstm and 10-fold cross validation, 2019 International Conference on Signal Processing and Communication (ICSC), 323-328.
[44] Hashish, I.A., Forni, F., Andreotti, G., Facchinetti, T., Darjani, Sh., 2019, A hybrid model for bitcoin prices prediction using hidden Markov models and optimized LSTM networks, 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 721-728.
[45] Yiying, W., Yeze, Z., 2019, Cryptocurrency price analysis with arti cial intelligence, 2019 5th International Conference on Information Management (ICIM), 97-101.
[46] Python releases for windows, url = https://www.python.org/downloads/windows
[47] McKinney, W., and and others, 2010, Data structures for statistical computing in python, Proceedings of the 9th Python in Science Conference, 445, 51-56.
[48] van der Walt, S., Colbert, G., 2011, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science and Engineering, 13, 22, Doi: 10.1109/M-CSE.2011.37.
[49] Hunter, J.D., Colbert, G., 2007, Matplotlib: A 2D graphics environment, Computing in science & engineering, 9(3), 90-95, Doi: 10.1109/MCSE.
[50] Lib: Technical analysis library, url = www.ta-lib.org.
[51] Chollet, F., 2016, Building powerful image classi cation models using very little data, Keras Blog, 5, 90-95, Doi: 10.1109/MCSE.
[52] Abadi, M. and Coauthors, 2016, Tensor ow: Large-scale machine learning on heteroge-neous distributed systems, arXiv preprint arXiv:1603.04467.
[53] Pedregosa, F. and Coauthors, 2011, Scikit-learn: Machine learning in Python, the Journal of machine Learning research, 12, 25-30.
[54] Ho, T.K., 1995, Scikit-learn: Machine learning in Python, Proceedings of 3rd international conference on document analysis and recognition, 1, 278-282.
[55] Sethia, A., Raut, P., 2019, Application of lstm, gru and ica for stock price prediction, Information and Communication Technology for Intelligent Systems, 479-487.
[56] Chandrashekar, G., Sahin, F., 2014, A survey on feature selection methods, Computers & Electrical Engineering, 40(1), 16-28.
[57] Yamak, P.T., Yujian, L., Gadosey, P.K., 2019, A comparison between arima, lstm, and gru for time series forecasting, Proceedings of the 2019 2nd International Conference on Algorithms Computing and Arti cial Intelligence, 4955.