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

【Author】 Shang, Dawei; Guo, Ziyu; Wang, Hui

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】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.

【Keywords】Improved convolutional neural network; Attention-based allocation function; Cryptocurrency price; Interpretable machine learning approach; Times series

【发表时间】2024 SEP

【收录时间】2024-08-19

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测