<![CDATA[The cryptocurrency market, specifically Solana (SOL), exhibits extreme volatility that is difficult for conventional algorithms to predict accurately. Additionally, advanced Deep Learning models often act as black-boxes. This study implements the Temporal Fusion Transformer (TFT) architecture for robust and interpretable multi-horizon volatility prediction of SOL/USDT. High-frequency historical data (1-hour intervals) was acquired via Binance API from January 2023 to December 2024. Data preprocessing included technical indicator feature engineering, Z-Score normalization, and chronological data splitting. Hyperparameters were optimized using Optuna. The TFT architecture performed exceptionally, achieving a Root Mean Squared Error (RMSE) of 2.5191 USDT and a Coefficient of Determination (R2) of 0.9987. Probabilistically, the model successfully mapped risk ranges with a Weighted Mean Quantile Loss of 0.4629. Explainable AI analysis revealed that predictions were heavily influenced by historical closing price, RSI, Volume, and MACD. Innovatively, TFT outputs were integrated into a Decision Support System utilizing Google Gemini 2.5-Flash to automatically generate market analysis narratives. In conclusion, this study produced a robust forecasting model, successfully resolving the black-box problem into a transparent virtual analyst assistant.]]>
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