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

Chen Qiu ()
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Chen Qiu: Beijing Normal University-Hong Kong Baptist University United International College, Faculty of Science and Technology

A chapter in Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, pp 762-770 from Springer

Abstract: 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.

Keywords: Bitcoin price prediction; Holt's linear trend model; ARIMA model; Time series analysis; Epidemic impact (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-748-9_84

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DOI: 10.2991/978-94-6463-748-9_84

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