Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)

Using Four Models to Predict Bitcoin Price in the COVID-19 Period

Authors
Chen Qiu1, *
1Beijing Normal University-Hong Kong Baptist University United International College, Faculty of Science and Technology, Zhuhai, Guangdong Province, 519087, China
*Corresponding author. Email: s230024390@mail.uic.edu.cn
Corresponding Author
Chen Qiu
Available Online 3 July 2025.
DOI
10.2991/978-94-6463-748-9_84How to use a DOI?
Keywords
Bitcoin price prediction; Holt's linear trend model; ARIMA model; Time series analysis; Epidemic impact
Abstract

With the increasing influence of Bitcoin in the market, the prediction of Bitcoin price has also received a lot of attention. Accurately predicting Bitcoin prices is of great importance to both traders and investors. This research selects the Bitcoin price from 2020 to 2023 during the epidemic period for analyzing. Drift model, Naive model, Holt's linear trend method, and Autoregressive Integrated Moving Average (ARIMA) model are used to forecast the price of Bitcoin and the prediction results of these four models are compared. In this study, log transformation of data is performed first. The data from 2020–2022 is used as the training set and the data from 2022–2023 is used as the test set. The results show that in the training set, the ARIMA model has the smallest Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), values, which shows its excellent ability in simulating and learning data. However, in the test set, Holt's linear trend method has the highest prediction accuracy since it has the lowest MAE, Root Mean Square Error (RMSE), and MAPE values. This shows that during this particular period of the epidemic, simple trend models such as Holt's linear trend method can more accurately predict the price of bitcoin. This study explores the advantages and disadvantages of different time series models and the accuracy of prediction in the special epidemic period, which is helpful for further exploration in the future.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
3 July 2025
ISBN
978-94-6463-748-9
ISSN
2352-5428
DOI
10.2991/978-94-6463-748-9_84How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Chen Qiu
PY  - 2025
DA  - 2025/07/03
TI  - Using Four Models to Predict Bitcoin Price in the COVID-19 Period
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 762
EP  - 770
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_84
DO  - 10.2991/978-94-6463-748-9_84
ID  - Qiu2025
ER  -