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JCSIS

Temporal Fusion Transformer for Multi-Step Ahead Cryptocurrency Price Forecasting with Uncertainty Quantification

Sofia Arkhstan
Volume 5

Abstract

Cryptocurrency markets are characterized by extreme volatility, nonlinear price dynamics, strong temporal dependencies, and sensitivity to market sentiment, liquidity fluctuations, and macroeconomic uncertainty, making accurate multi-step ahead forecasting a highly challenging task. Traditional statistical models and conventional machine learning approaches often fail to capture long-range temporal patterns, regime shifts, and complex interactions among heterogeneous market indicators, while many deep learning models provide deterministic predictions without quantifying the uncertainty associated with future price movements. This paper proposes a Temporal Fusion Transformer (TFT)-based forecasting framework for multi-step ahead cryptocurrency price prediction with integrated uncertainty quantification. The proposed model combines recurrent sequence processing, interpretable attention mechanisms, gated residual networks, and variable selection layers to effectively learn temporal dependencies from both historical and known future inputs. The framework was evaluated using high-frequency and daily market data from major cryptocurrencies, including Bitcoin, Ethereum, and Binance Coin, incorporating multiple predictive features such as open, high, low, close, trading volume, technical indicators, volatility measures, moving averages, relative strength index, MACD, and market momentum signals. A comprehensive preprocessing pipeline was applied, including missing value handling, normalization, feature engineering, sliding-window sequence construction, and train-validation-test splitting based on chronological order to prevent data leakage. Unlike conventional point forecasting models, the proposed TFT framework produces probabilistic forecasts through quantile regression, enabling the estimation of prediction intervals and supporting risk-aware decision-making under market uncertainty. Experimental results demonstrate that the proposed model achieves superior forecasting performance compared with baseline models including ARIMA, SVR, Random Forest, LSTM, GRU, BiLSTM, and standard Transformer architectures across multiple forecasting horizons. The model records lower RMSE, MAE, and MAPE values while maintaining well-calibrated prediction intervals with strong coverage probability. Furthermore, the attention-based interpretability mechanism reveals the relative importance of historical price trends, volatility indicators, trading volume, and momentum-based features in driving future cryptocurrency price movements. The findings indicate that the proposed TFT-based framework provides an accurate, interpretable, and uncertainty-aware solution for cryptocurrency price forecasting, offering practical value for traders, portfolio managers, financial analysts, and automated decision-support systems operating in highly volatile digital asset markets.

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Keywords

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