Cryptocurrency Price Prediction Using Machine Learning
Abstract
The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.
References
[2] Snega . S, Nivedha . B, Ramachandran . C. A , “ Bitcoin Price Prediction using ML”,B.S Abdur Rahman Crescent Institute Of Science and Technology, Chennai, India, 2023
[3] Gurupradip G, Harishvaran m,Amsavalli, “Cryptocurrency Price Prediction Using Machine Learning”, International Journal Of Engineering and Technology and Communication Engineering, Chennai India, VoL 12, Issue 4, April 2023
[4] Shubham Bhattad, Stefin Sunnymon, “Review Of Machine Learning for cryptocurrency Price Prediction,” Dept. Of Information Technology,Xavier Institute Of Engineering ,Mumbai India, 17 May 2023
[5] Ranganath Maruti Bhajantri, Rohith F Puttappanavar, Shashank, Vishal, Prof. Bhaskar Rao, “Gold Price Prediction Using Machine Learning”, Department of Computer Science and Engineering, Bangalore, Karnataka, India, Vol-9, Issue-3, 2023
[6] Dylan Norbert Gono, Herlina Napitupulu, Firdaniza, “Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method”, Department of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia, 5 September 2023
[7] Ram Prasad S K, Dr. Vibha M B, “A Study On Gold Price Prediction Using Machine Learning”, Department of MCA, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India, Vol-11, Issue 06 2023
[8] N. Maleki, A. Nikoubin, M. Rabbani, Y. Zeinali, “Bitcoin Price Prediction based on Other cryptocurrencies using Machine learning and Time series analysis”, University of Technology, Tehran, Iran, 2023
[9] Pratik Jaquart, Sven Kopke, Christof Weinhardt , “Machine Learning For cryptocurrency and market Trading,” Institute of Information Systems and Marketing Germany, pp. 331–352, 14 December 2022.
[10] Ashmit K. Khobragade, Omkar C. Keskar, “Forecasting of Cryptocurrency Values Using Machine Learning,” International Journal Of Scientific & Technology, Marathwada Mitra Mandals College of Engineering, Pune, VOL 10 Issue 5 May 2022
[11] Adena Wahyu Gumelar, Tacbir Hendro Pudjiantoro, Puspita Nurual Sabrina “NFT Coin Price Prediction (Non-Fungible Token) Using K-Nearest Neighbors Method ”, Informatics Department Faculty of Science and Informatics Universitas Jenderal Achmad Yani Cimahi, Indonesia, International Conference on Industrial Engineering and Operations Management, Johor Bahru, Malaysia, September 13-15, 2022
[12] Chodavrapu Pragna, Bhavan Purra, Adari Viharika, Garabhapu Roshni, Prof. B. Prajna, “Gold Price Prediction Using Machine Learning”, Andhra University College Of Engineering For Women, Visakhapatnam, Andhra Pradesh, India, Vol- 4, Issue 5, May 2022
[13] Rushikesh Ghule, Abhijeet Gadhave, Prof. Manish Dubey, Dr. Jyoti Kharade, “Gold Price Prediction Using Machine Learning”, Bharati Vidyapeeth’s Institute of Management and Information Technology, Navi Mumbai, Vol-06, Issue-06, June 2022
[14] Hamed Taherdoost, “Non-Fungible Tokens(NFT): A Systematic Review”, Department of Arts, Communication and Social Science, University Canada West, Vancouver, 31 December 2022
[15] Kin-Hon Ho, Tse-Tin Chan, Haoyuan Pan, “Analysis of Non-Fungible Token Pricing Factors with Machine Learning”, University of Hong Kong, Octomber 2022
[16] Florian Horky, Carolina Rachel, Jarko Fidrmuc, “Price Determinants of Non-Fungible Tokens in Digital Art Market”, May 2022
[17] Perry Sadorsky, “Predicting Gold and Silver Price Dieextion Using Tree-Based Classifiers”, Schulich School of Business, York University, Toronto, 29 April 2021
[18] Mrs Vaidehi M, Alivia Pandit, Bhaskar Jindal, Minu Kumari, Rupali Singh , “Bitcoin Price Prediction Using Machine Learning”,International Journal Of Engineering and Technology and Management Research, Bangalore India III, pp. 20–28,13 May 2021.
[19] M. Sravani, Ch. Abhilash, T. Divya, Ch. Vasthav, D.Priyanka, “Gold Price Prediction”, Department of CSE, Gudlavalleru Engineering College, Gudlavalleru, India, Vol- 9, Issue 6 June 2021
[20] Ilan Alon, “Predictors of NFT Prices: An Automated Machine Learning Approach”, University of Ariel, Israel, Vol- 31, Issue 1, 2021
[21] Nandini Tripurana, Binodini Kar, Sujata Chakravarty, Bijay K. Paikaray, Suneeta Saptpathy, “Gold Price Prediction Using Machine Learning Techniques”, University of Technology and management, Odisha, India, 2021
[22] Lekkala Sreekanth Reddy ,Dr . P. Sriramya , “A research On Bitcoin Price Prediction Using Machine Learning Algorithms”, International Journal Of Scientific & Technology Research, Vol 9, 4 April 2020.
[23] Lokesh Vaddi, Vaishnavi Neelisetty, “Predicting Crypto Currency Prices Using Machine Learning And deep Learning”,International Journal Of Advanced Trends in Computer Science and Engineering, VOL 9,4 July 2020
[24] N. Maleki, A . Nikoubin , M. Rabbani, “Bitcoin Price Prediction Based on other Cryptocurrencies Using machine Learning and tinme Series Analysis., Sharif University Of Technology, Iran, pp.285-301, 2020
[25] Behshad Jodeiri Shokri, Hesam Dehghani, Reza Shamsi, “Predicting silver price by applying a copled multiple linear regression (MLR) and imperialist competitive algorithm (ICA)”, Department of mining Engineering, Hamedan University of Technology, Hamedan, Iran, Vol. 1, No. 1, June 2020
[26] K. Ramya Laxmi, Marri Abhinandhan Reddy, CH. Shivasai, P. Sandeep Reddy, “Cryptocurrency Price Prediction Using Machine Learning”, Department of CSE, Sreyas Institute of Engineering and Technology, Nagole, Hyderabad, India, SAMRIDDHI Volume 12, Special Issue 3,2020
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AUTHOR RESPONSIBILITIES
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
Author/Employer Rights
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
Reprint/Republication Policy
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
GENERAL TERMS
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :
Creative Commons Attribution 4.0 International License.
