In this article, we introduce a novel deep learning hybrid model that
integrates attention Transformer and Gated Recurrent Unit (GRU) architectures
to improve the accuracy of cryptocurrency price predictions. By combining the
Transformer’s strength in capturing long-range patterns with the GRU’s ability
to model short-term and sequential trends, the hybrid model provides a
well-rounded approach to time series forecasting. We apply the model to predict
the daily closing prices of Bitcoin and Ethereum based on historical data that
include past prices, trading volumes, and the Fear and Greed index. Valutiamo
the performance of our proposed model by comparing it with four other machine
learning models: two are non-sequential feedforward models: Radial Basis
Function Network (RBFN) and General Regression Neural Network (GRNN), and two
are bidirectional sequential memory-based models: Bidirectional Long-Short-Term
Memory (BiLS™) and Bidirectional Gated Recurrent Unit (BiGRU). The performance
of the model is assessed using several metrics, including Mean Squared Error
(MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean
Absolute Percentage Error (MAPE), along with statistical validation through the
nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test.
The results demonstrate that our hybrid model consistently achieves superior
accuracy, highlighting its effectiveness for financial prediction tasks. Questi
findings provide valuable insights for improving real-time decision making in
cryptocurrency markets and support the growing use of hybrid deep learning
models in financial analytics.

Questo articolo esplora i giri e le loro implicazioni.

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