Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum

To predict Ethereum price fluctuations, this study proposes a new two-stage Machine Learning approach using an improved convolutional neural network and a recurrent neural network framework, integrating an attention mechanism-based distribution function algorithm. We construct a dataset and perform model training, fitting, and forecasting. The results indicate that compared with traditional neural networks and time-series models such as GRU and ARIMA, respectively, this approach can effectively use the data information of digital cryptocurrency and improve the prediction accuracy and interpretability of attention-based allocation functions. This study contributes to the literature by offering a new approach for stakeholders.