ARIMA-GARCH Based Time Series Analysis of Cryptocurrency Volatility - Husin, Husna Sarirah; Yong, Yap Kah; Yodi; Sangodiah, Anbuselvan; Seng, Steven Eu Kok; Lalitha, R (Journal of Soft Computing and Data Mining, 2026)
Cryptocurrencies are a new type of asset that is changing the game. They are decentralized, very volatile, and their prices change quickly. Investors, policymakers, and researchers all need to know how bitcoin markets work. This article examines the volatility patterns of significant cryptocurrencies by employing the Autoregressive Integrated Moving Average (ARIMA) in conjunction with the GARCH model to assess historical price data and predict future trends. We also do a full volatility analysis to show how unpredictable cryptocurrency markets are. We also do a seasonality analysis and calculate the Relative Strength Index (RSI) for the technical analysis indicators. The study uses the R programming environment to clean up data, model time series, and check performance indicators. Our results show that ARIMA models accurately capture the time-based relationships in bitcoin time series, which makes them good for long-term predictions.