Predicting BTC and ETH Prices Using Time Series Models

Xinyi Wang ()
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Xinyi Wang: Sino-French Institute, Renmin University of China

A chapter in Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, pp 753-761 from Springer

Abstract: Abstract Cryptocurrencies are difficult to be replaced by traditional currencies due to their numerous advantages. The uncertainty of their fluctuations makes cryptocurrency prediction valuable yet challenging, and an effective prediction model can provide investors with many decision-making bases. In this study, the daily closing prices of Bitcoin and Ethereum from 2020 to 2022 were used as the training set, and the closing prices in 2023 were used as the test set to explore and compare the prediction effects of five models: Naive Method, Drift Method, Error, Trend, and Seasonal (ETS) Method, Damped Holt’s Method, and Autoregressive Integrated Moving Average (ARIMA). The analysis found that the datasets have strong trend characteristics and weak seasonality. In the prediction, the Drift Method and Damped Holt’s Method fit these characteristics better and thus have good prediction effects. Moreover, the strong flexibility of the ARIMA model usually enables it to make appropriate adjustments for specific datasets. However, models that rely too much on seasonal characteristics show large residuals and are not suitable for predicting cryptocurrency prices.

Keywords: Cryptocurrency; Time series; Forecast; Seasonality (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_83

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DOI: 10.2991/978-94-6463-748-9_83

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