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Fusion: Practice and Applications
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Title

A Comparative Analysis and Prediction over Bitcoin Price Using Machine Learning Technique

Authors Names :   Meenu Gupta   1 *     Riya Srivastava   2  

1  Affiliation :  Chandigarh University, INDIA

    Email :  meenu.e9406@cumail.in


2  Affiliation :  Chandigarh University, INDIA

    Email :  cu.17bcs1711@gmail.com



Doi   :   https://doi.org/10.54216/FPA.050103

Received: January 17, 2021 Accepted: July 02, 2021

Abstract :

Bitcoin is one of the primary computerized monetary forms to utilize peer innovation to work with moment installments. The free people and organizations who own the overseeing figuring control and take part in the bitcoin network—bitcoin "miners"— are accountable for preparing the exchanges on the blockchain and are persuaded by remunerations (the arrival of new bitcoin) and exchange charges paid in bitcoin. These excavators can be considered as the decentralized authority implementing the believability of the bitcoin network. New bitcoin is delivered to the excavators at a fixed yet occasionally declining rate. There is just 21 million bitcoin that can be mined altogether. As of January 30, 2021, there are around 18,614,806 bitcoin in presence and 2,385,193 bitcoin left to be mined. This paper will predict the nature of bitcoin price because according to the reports of the past few years. The year 2020-present appeared to be a good time for bitcoin because, during this time duration, bitcoin has seen huge ups and downs. This paper will use various Machine Learning Techniques for the predictive analysis of bitcoin to accurately predict the price's nature. As the price of bitcoin depends upon various factors, and these factors directly affect the price, i.e., multiple factors of bitcoin are dependent on each other. After analyzing the results from multiple research papers and review papers, we discovered each algorithm has its advantages and disadvantages when predicting the bitcoin value. Keeping in mind all the findings, we will find algorithms that predict the bitcoin price accurately and without fewer disadvantages. So, if we go as per assumptions, regression would be the best choice for predicting the bitcoin value, but there are others algorithms also. So, in this paper, we will see the results of the multiple algorithms and then choose the correct algorithm after analyzing the results of all the implemented algorithms. This paper also includes the implementation of the comparison charts with each algorithm so that it will be easy to analyze the findings of each algorithm.

Keywords :

Regression; Machine Learning; Bitcoin; Algorithms; Predictive Analysis; Accuracy; Exploratory Data Analysis

References :

[1]                Velankar, S., Valecha, S., & Maji, S. (2018, February). Bitcoin price prediction using machine learning. In 2018 20th International Conference on Advanced Communication Technology (ICACT) (pp. 144-147). IEEE.

[2]                L. Tan, K. Yu, N. Shi, C. Yang, W. Wei, and H. Lu, "Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach," IEEE Transactions on Network Science and Engineering,

[3]                L. Tan, N. Shi, K. Yu, M. Aloqaily, Y. Jararweh, “A Blockchain-Empowered Access Control Framework for Smart Devices in Green Internet of Things”, ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-20, 2021,

[4]                K. Yu, L. Tan, M. Aloqaily, H. Yang, and Y. Jararweh, “Blockchain-Enhanced Data Sharing with Traceable and Direct Revocation in IIoT”, IEEE Transactions on Industrial Informatics

[5]                K. Yu, L. Tan, X. Shang, J. Huang, G. Srivastava, and P. Chatterjee, "Efficient and Privacy-Preserving Medical Research Support Platform Against COVID-19: A Blockchain-Based Approach", IEEE Consumer Electronics Magazine,

[6]                L. Tan, H. Xiao, K. Yu, M. Aloqaily, Y. Jararweh, “A Blockchain-empowered Crowdsourcing System for 5G-enabled Smart Cities”, Computer Standards & Interfaces, 

[7]                C. Feng et al., "Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach," IEEE Network, vol. 35, no. 1, pp. 130-137, January/February 2021

[8]                N. Shi, L. Tan, W. Li, X. Qi, K. Yu, “A Blockchain-Empowered AAA Scheme in the Large-Scale HetNet”, Digital Communications and Networks, 

[9]                Z. Guo, L. Tang, T. Guo, K. Yu, M. Alazab, A. Shalaginov, “Deep Graph Neural Network-based Spammer Detection Under the Perspective of Heterogeneous Cyberspace”, Future Generation Computer Systems, https://doi.org/10.1016/j.future.2020.11.028. 

[10]             Z. Guo, Y. Shen, A. K. Bashir, M. Imran, N. Kumar, D. Zhang, and K. Yu, "Robust Spammer Detection Using Collaborative Neural Network in Internet of Thing Applications", IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9549-9558, June 15, 2021

[11]             K. Yu, L. Tan, X. Shang, J. Huang, G. Srivastava, and P. Chatterjee, "Efficient and Privacy-Preserving Medical Research Support Platform Against COVID-19: A Blockchain-Based Approach", IEEE Consumer Electronics Magazine,

[12]             Z. Guo, K. Yu, A. Jolfaei, A. K. Bashir, A. O. Almagrabi, and N. Kumar, “A Fuzzy Detection System for Rumors through Explainable Adaptive Learning”, IEEE Transactions on Fuzzy Systems,

[13]             Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395.

[14]              Phaladisailoed, T., & Numnonda, T. (2018, July). Machine learning models comparison for bitcoin price prediction. In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 506-511). IEEE.

[15]              https://www.kaggle.com/prakhargupta231/bitcoin-price-arima/edit 

[16]              Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898.

[17]              Azari, A. (2019). Bitcoin price prediction: An ARIMA approach. arXiv preprint arXiv:1904.05315.

[18]             M. Daniela and A. BUTOI, ―Data mining on Romanian stock market using neural networks for price prediction‖. Informatica Economica, 17,2013.

[19]             Herrera-Joancomartí J. (2015) Research and Challenges on Bitcoin Anonymity. In: Garcia-Alfaro J. et al. (eds) Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance. DPM 2014, QASA 2014, SETOP 2014. Lecture Notes in Computer Science, vol 8872. Springer.

[20]             Androulaki, E., Karame, G., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In Sadeghi, A.R., ed.: Financial Cryptography and Data Security. Volume 7859 of Lecture Notes in Computer Science. Springer Berlin Heidelberg (2013)

[21]             Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In Sadeghi, A.R., ed.: Financial Cryptography and Data Security. Volume 7859 of Lecture Notes in Computer Science. Springer Berlin Heidelberg (2013)

[22]             Spagnuolo, M., Maggi, F., Zanero, S.: Bitiodine: Extracting intelligence from the bitcoin network. In Christin, N., Safavi-Naini, R., eds.: Financial Cryptography and Data Security. Volume 8437 of Lecture Notes in Computer Science. Springer Berlin Heidelberg (2014)

[23]             Rane, P. V., & Dhage, S. N. (2019, March). Systematic erudition of bitcoin price prediction using machine learning techniques. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 594-598). IEEE.

[24]             Madan, I., Saluja, S., & Zhao, A. (2015). Automated bitcoin trading via machine learning algorithms. URL: http://cs229. Stanford. edu/proj2014/Isaac% 20Madan, 20.

[25]             https://www.kaggle.com/mczielinski/bitcoin-historical-data

[26]             Lamothe-Fernández, P., Alaminos, D., Lamothe-López, P., & Fernández-Gámez, M. A. (2020). Deep learning methods for modeling bitcoin price. Mathematics, 8(8), 1245.

[27]             Awoke, T., Rout, M., Mohanty, L., & Satapathy, S. C. (2021). Bitcoin price prediction and analysis using deep learning models. In Communication Software and Networks (pp. 631-640). Springer, Singapore.

 

[28]             Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, 13(2), 23.


Cite this Article as :
Meenu Gupta , Riya Srivastava, A Comparative Analysis and Prediction over Bitcoin Price Using Machine Learning Technique, Fusion: Practice and Applications, Vol. 5 , No. 1 , (2021) : 31-41 (Doi   :  https://doi.org/10.54216/FPA.050103)