Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction
To address the low accuracy of short-term cryptocurrency price forecasting caused by high volatility and non-stationarity, this paper proposes an Adaptive Temporal Fusion Transformer (ATFT) framework. The method introduces three key innovations: (1) a dynamic subsequence partitioning mechanism based on relative extrema; (2) subsequence categorization according to fixed-pattern endings to decouple and model distinct market response patterns; and (3) a multi-branch Temporal Fusion Transformer architecture that integrates initial trend features to enable conditional forecasting. Evaluated on 10-minute ETH-USDT data, ATFT achieves statistically significant improvements over standard TFT and LSTM in both prediction error (MAE/RMSE) and simulated trading profitability. These results empirically validate the effectiveness of pattern-aware modeling for high-frequency cryptocurrency asset forecasting.