BITCOIN PRICE PREDICTION USING MACHINE LEARNING

Authors

  • Mrs. Vaidehi M Assistant Professor, Dept. of Information Science and Engineering, DSCE, Bangalore, India
  • Alivia Pandit Students of Dept. of Information Science and Engineering, DSCE, Bangalore, India https://orcid.org/0000-0001-5706-9576
  • Bhaskar Jindal Students of Dept. of Information Science and Engineering, DSCE, Bangalore, India https://orcid.org/0000-0001-7068-7651
  • Minu Kumari Students of Dept. of Information Science and Engineering, DSCE, Bangalore, India
  • Rupali Singh Student https://orcid.org/0000-0002-6625-4041

DOI:

https://doi.org/10.29121/ijetmr.v8.i5.2021.953

Keywords:

Bitcoin, Cryptocurrency, Machine learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction.

Abstract

In this paper, we use the LSTM version of Recurrent Neural Networks, pricing for Bitcoin. To develop a better understanding of its price influence and a common view of this good invention, we first give a brief overview of Bitcoin again economics. After that, we define the database, including data from stock market indices, sentiment, and . in this investigation, we demonstrate the use of LSTM structures with the series of time mentioned above. In conclusion, we draw the Bitcoin pricing forecast results 30 and 60 days in advance.

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References

Kathyayini R S , D G Jyothi,”Crypto-Currency Price Prediction using Machine Learning”,International Journal of Advanced Research in Computer and Communication Engineering(IJARCCE)

Kejsi Struga, Olti Qirici,”Bitcoin Price Prediction with Neural Networks”,http://ceur- ws.org/Vol-2280/paper-06.pdf.

M. Amjad and D. Shah, "Trading Bitcoin and Online Time Series Prediction," in NIPS 2016 Time Series Workshop, 2017.

D. Garcia and F. Schweitzer, "Social signals and algorithmic trading of Bitcoin," Royal Society Open Science, vol. 2, no. 9, 2015. DOI: https://doi.org/10.1098/rsos.150288

R. Chen and M. Lazer, "Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement," Stanford Computer Science, no. 229, 2011, p. 15.

A. Go, L. Huang and R. Bhayani, "Twitter Sentiment Classification using Distant Supervision," Stanford Computer Science, 2009.

B. Pang, L. Lee and S. Vaithyanathan, "Thumbs up: sentiment classification using machine learning techniques," in ACL-02 conference on Empirical methods in natural language processing, Philadelphia, PA, USA, 2002. DOI: https://doi.org/10.3115/1118693.1118704

M. Dixon, D. Klabjan and J. H. Bang, "Classification-based financial markets prediction using deep neural networks," ArXiv, 2017. DOI: https://doi.org/10.2139/ssrn.2756331

S. McNally, J. Roche and S. Caton, "Predicting the price of Bitcoin using machine learning," in 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2018. DOI: https://doi.org/10.1109/PDP2018.2018.00060

M. Daniela and A. BUTOI, "Data mining on Romanian stock market using neural networks for price prediction," Informatica Economica, vol. 17, no. 3, 2013. DOI: https://doi.org/10.12948/issn14531305/17.3.2013.11

D. Shah and K. Zhang, "Bayesian regression and Bitcoin," in 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015. DOI: https://doi.org/10.1109/ALLERTON.2014.7028484

H. Jang and J. Lee, "An Empirical Study on Modelling and Prediction of Bitcoin Prices with Bayesian Neural Networks based on Blockchain Information," in IEEE Early Access Articles, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2779181

Published

2021-06-02

How to Cite

M, V., Pandit, A., Jindal, B., Kumari, M., & Singh, R. (2021). BITCOIN PRICE PREDICTION USING MACHINE LEARNING. International Journal of Engineering Technologies and Management Research, 8(5), 20–28. https://doi.org/10.29121/ijetmr.v8.i5.2021.953