Research on Price Prediction of Digital Currency Based on Machine Learning

Research Article
Open access

Research on Price Prediction of Digital Currency Based on Machine Learning

Lirui Liu 1*
  • 1 Ranney School    
  • *corresponding author 2024liul@myranney.org
Published on 10 November 2023 | https://doi.org/10.54254/2754-1169/41/20232057
AEMPS Vol.41
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-83558-103-2
ISBN (Online): 978-1-83558-104-9

Abstract

Since the invention of digital currency, the social recognition and demand for special currency and similar cryptocurrencies have increased significantly with the development of digital currency and blockchain technology. The rapid rise in the price of digital currency and its significant volatility attracts a large number of users to invest in it as a digital asset. Before the formation of a regulatory strategy with a standardized system, the development of digital currency will undoubtedly have an increasing impact on society, and its price fluctuation will become an unstable factor in society by increasing the risk to users. Therefore, finding out the factors that affect the price of digital currency and forecasting its price has become the focus of research on Bitcoin in recent years. This will not only help investors and relevant institutions understand Bitcoin and the digital currency market but also help improve the financial market and its policies. In this paper, I use a machine learning model to predict the price of digital currency from both numerical and trend aspects. The main contents include the following elements.First, in terms of data characteristics, I comprehensively summarized and referred to the previous research on the price of special currency at home and abroad. Second, in terms of model prediction, I designed a two-stage feature processing method, used three kinds of recurrent neural network models to compare and predict the price of digital currency, and used recursive feature elimination (RFE) and logical regression (LR), random forest (RF), linear discriminant analysis (LDA) Support Vector Machine (SVM) and Naive Bayes (NB), five commonly used machine learning models, are combined to predict the price trend of Bitcoin.

Keywords:

digital currency, price forecasting, machine learning, artificial neural network

Liu,L. (2023). Research on Price Prediction of Digital Currency Based on Machine Learning. Advances in Economics, Management and Political Sciences,41,135-142.
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References

[1]. Yenidoğan I, Çayir A, Kozan O, et al. Bitcoin forecasting using ARIMA and PROPHET[C]//2018 3rd international conference on computer science and engineering (UBMK). IEEE, 2018: 621-624.

[2]. Saad M, Choi J, Nyang D H, et al. Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions[J]. IEEE Systems Journal, 2019, 14(1): 321-332.

[3]. Zhengyang W, Xingzhou L, Jinjin R, et al. Prediction of cryptocurrency price dynamics with multiple machine learning techniques[C]//Proceedings of the 2019 4th International Conference on Machine Learning Technologies. 2019: 15-19.

[4]. Livieris I E, Kiriakidou N, Stavroyiannis S, et al. An advanced CNN-LSTM model for cryptocurrency forecasting[J]. Electronics, 2021, 10(3): 287.

[5]. Jaquart P, Dann D, Weinhardt C. Short-term bitcoin market prediction via machine learning[J]. The Journal of Finance and Data Science, 2021, 7: 45-66.

[6]. Biswas S, Pawar M, Badole S, et al. Cryptocurrency price prediction using neural networks and deep learning[C]//2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021, 1: 408-413.

[7]. Li Y. The price prediction of virtual currency base on improved support vector regression[C]//2021 4th International Conference on Information Systems and Computer Aided Education. 2021: 2587-2591.

[8]. Jiang H. Cryptocurrency price forecasting based on shortterm trend KNN model[C]//2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2021: 1165-1169.

[9]. Li Jing. Building a Bitcoin Market Forecast Model Using BP Neural Network [J]. Monthly Journal of Finance and Accounting, 2016 (21): 33-36.

[10]. Li Yinglu. Prediction of Cryptocurrency Index Based on BP Neural Network [J]. Market Weekly, 2019 (08): 104-105.

[11]. He Xiongwei, Lin Hai. Empirical Analysis of Time Series Prediction of Bitcoin Based on LSTM [J]. Modern Computer, 2020 (36): 40-46.

[12]. Zhao Lei, Liu Qing. Risk Identification of Bitcoin Price foam Based on LPPL Model [J]. Statistics and Decision, 2020,36 (18): 128-131.

[13]. Zhang Ning, Fang Jingwen, Zhao Yuxuan. Bitcoin price prediction based on LSTM hybrid model [J]. Computer Science, 2021,48 (S2): 39-45.

[14]. Bai Wankuan. Research and Application of RNN Neural Network in Stock Index Price Forecasting Model [D]. Chongqing University, 2018.

[15]. Bao Zhenshan, Guo Junnan, Xie Yuan, Zhang Wenbo. Prediction Model of Stock Price Rise and Fall Based on LSTM-GA [J]. Computer Science, 2020, 47 (S1): 467-473.

[16]. Gunduz, H., Yaslan, Y., & Cataltepe, Z. Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations[J]. Knowledge Based Systems, 2017, 137:138–148.

[17]. Geng Jingjing, Liu Yumin, Li Yang, Zhao Zheyun. Prediction Model of Stock Index Based on CNN-LSTM [J]. Statistics and Decision Making, 2021,37 (05): 134-138.

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

[19]. Mcnally S, Roche J, Caton S . Predicting the Price of Bitcoin Using Machine Learning. 2018:339-343.

[20]. Fischer, T., & Krauss, C. Deep learning with long short-term memory networks for financial market predictions[J]. European Journal of Operational Research, 2018, 270(2):654–669.

[21]. Mallqui, D. C. A., & Fernandes, R. A. S. Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques[J]. Applied Soft Computing Journal, 2019, 75:596–606.

[22]. Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning[J]. Entropy, 2019, 21(6).

[23]. Li Zhongchen. Research on Stock Trend Prediction Based on Machine Learning [D]. University of Electronic Science and Technology of China, 2020.

[24]. Zhang Guisheng, Zhang Xindong. Research on SVM-GARCH Stock Price Forecasting Model Based on Neighborhood Mutual Information[J]. China Management Science, 2016,24 (09): 11-20.

Cite this article

Liu,L. (2023). Research on Price Prediction of Digital Currency Based on Machine Learning. Advances in Economics, Management and Political Sciences,41,135-142.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 7th International Conference on Economic Management and Green Development

ISBN：978-1-83558-103-2(Print) / 978-1-83558-104-9(Online)
Editor：Canh Thien Dang
Conference website: https://www.icemgd.org/
Conference date: 6 August 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.41
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Yenidoğan I, Çayir A, Kozan O, et al. Bitcoin forecasting using ARIMA and PROPHET[C]//2018 3rd international conference on computer science and engineering (UBMK). IEEE, 2018: 621-624.

[2]. Saad M, Choi J, Nyang D H, et al. Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions[J]. IEEE Systems Journal, 2019, 14(1): 321-332.

[3]. Zhengyang W, Xingzhou L, Jinjin R, et al. Prediction of cryptocurrency price dynamics with multiple machine learning techniques[C]//Proceedings of the 2019 4th International Conference on Machine Learning Technologies. 2019: 15-19.

[4]. Livieris I E, Kiriakidou N, Stavroyiannis S, et al. An advanced CNN-LSTM model for cryptocurrency forecasting[J]. Electronics, 2021, 10(3): 287.

[5]. Jaquart P, Dann D, Weinhardt C. Short-term bitcoin market prediction via machine learning[J]. The Journal of Finance and Data Science, 2021, 7: 45-66.

[6]. Biswas S, Pawar M, Badole S, et al. Cryptocurrency price prediction using neural networks and deep learning[C]//2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021, 1: 408-413.

[7]. Li Y. The price prediction of virtual currency base on improved support vector regression[C]//2021 4th International Conference on Information Systems and Computer Aided Education. 2021: 2587-2591.

[8]. Jiang H. Cryptocurrency price forecasting based on shortterm trend KNN model[C]//2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2021: 1165-1169.

[9]. Li Jing. Building a Bitcoin Market Forecast Model Using BP Neural Network [J]. Monthly Journal of Finance and Accounting, 2016 (21): 33-36.

[10]. Li Yinglu. Prediction of Cryptocurrency Index Based on BP Neural Network [J]. Market Weekly, 2019 (08): 104-105.

[11]. He Xiongwei, Lin Hai. Empirical Analysis of Time Series Prediction of Bitcoin Based on LSTM [J]. Modern Computer, 2020 (36): 40-46.

[12]. Zhao Lei, Liu Qing. Risk Identification of Bitcoin Price foam Based on LPPL Model [J]. Statistics and Decision, 2020,36 (18): 128-131.

[13]. Zhang Ning, Fang Jingwen, Zhao Yuxuan. Bitcoin price prediction based on LSTM hybrid model [J]. Computer Science, 2021,48 (S2): 39-45.

[14]. Bai Wankuan. Research and Application of RNN Neural Network in Stock Index Price Forecasting Model [D]. Chongqing University, 2018.

[15]. Bao Zhenshan, Guo Junnan, Xie Yuan, Zhang Wenbo. Prediction Model of Stock Price Rise and Fall Based on LSTM-GA [J]. Computer Science, 2020, 47 (S1): 467-473.

[16]. Gunduz, H., Yaslan, Y., & Cataltepe, Z. Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations[J]. Knowledge Based Systems, 2017, 137:138–148.

[17]. Geng Jingjing, Liu Yumin, Li Yang, Zhao Zheyun. Prediction Model of Stock Index Based on CNN-LSTM [J]. Statistics and Decision Making, 2021,37 (05): 134-138.

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

[19]. Mcnally S, Roche J, Caton S . Predicting the Price of Bitcoin Using Machine Learning. 2018:339-343.

[20]. Fischer, T., & Krauss, C. Deep learning with long short-term memory networks for financial market predictions[J]. European Journal of Operational Research, 2018, 270(2):654–669.

[21]. Mallqui, D. C. A., & Fernandes, R. A. S. Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques[J]. Applied Soft Computing Journal, 2019, 75:596–606.

[22]. Valencia, F., Gómez-Espinosa, A., & Valdés-Aguirre, B. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning[J]. Entropy, 2019, 21(6).

[23]. Li Zhongchen. Research on Stock Trend Prediction Based on Machine Learning [D]. University of Electronic Science and Technology of China, 2020.

[24]. Zhang Guisheng, Zhang Xindong. Research on SVM-GARCH Stock Price Forecasting Model Based on Neighborhood Mutual Information[J]. China Management Science, 2016,24 (09): 11-20.

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