<![CDATA[The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.]]>
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