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Using Regression and Algorithms in Artificial Intelligence to Predict the Price of Bitcoin

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

Cryptocurrency is a topic that is no longer strange in the investment world. Bitcoin is considered a very famous cryptocurrency and has a large amount of investment across the globe. Therefore, in recent years, the Bitcoin investment field has been attracting much research to help investors in this field maximize profits. In this study, using regression and algorithms in artificial intelligence such as K-Nearest Neighbors (K-NN), Neural Network (NN), Decision Tree (DT), Support Vector Machines (SVM), Random Forest (RF), and Linear Regression (LR) to predict the opening price of Bitcoin. We are using the hybrid model of the LR algorithm with K-NN, NN, DT, SVM, and RF algorithms to improve Bitcoin price prediction performance. The study results show that most algorithms predict well, and the hybrid model has better prediction results. This prediction result shows that the hybrid model has the potential to be applied in practice to improve the accuracy of Bitcoin opening price prediction.

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Acknowledgments

This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number DS2022-26-23.

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Authors and Affiliations

  1. University of Information Technology, VNU-HCM, Ho Chi Minh, Vietnam

    Nguyen Dinh Thuan & Nguyen Thi Viet Huong

Authors
  1. Nguyen Dinh Thuan
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  2. Nguyen Thi Viet Huong
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Corresponding author

Correspondence to Nguyen Dinh Thuan .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Thuan, N.D., Huong, N.T.V. (2023). Using Regression and Algorithms in Artificial Intelligence to Predict the Price of Bitcoin. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_29

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