Abstract

The growing potential and high volatility of the cryptocurrency market attract a lot of interest from both businesses and investors. Even though the prices fluctuate, predicting with time serious models such as ARMA and ARIMA would still provide a useful reference for analyzing the market. Recent studies on machine learning methods including RNNs have made new progress in forecasting digital currencies. This study focuses on one of the traditional models ARMA to predict the time serious dataset from 2021-2022 of cryptocurrencies including Bitcoin, Ethereum and Ripple. To be specific, AIC and ADF tests are used to choose the optimal model and suitable dataset. According to the analysis, the ARMA model would be affected by the volatility of Bitcoin. However, the predictions are not precise enough but still a valuable reference for certain businesses and individual investors. More state-of-art machine learning models can be utilized in future study to enhance the performance. Overall, these results shed light on guiding further exploration of crypto currency price prediction.

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