This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extract significant features from raw cryptocurrency data. The application of technical indicators, such facilitates the extraction of intricate patterns, momentum, volatility, and trends. The Performer neural network, employing Fast Attention Via positive Orthogonal Random features (FAVOR+), has demonstrated superior computational efficiency and scalability compared to the traditional Multi-head attention mechanism in Transformer models. Additionally, the integration of BiLSTM in the feedforward network enhances the model's capacity to capture temporal dynamics in the data, processing it in both forward and backward directions. This is particularly advantageous for time series data where past and future data points can influence the current state. The proposed method has been applied to the hourly and daily timeframes of the major cryptocurrencies and its performance has been benchmarked against other methods documented in the literature. The results underscore the potential of the proposed method to outperform existing models, marking a significant progression in the field of cryptocurrency price prediction.

翻译：本研究提出了一种创新的方法用于预测加密货币时间序列，具体聚焦于比特币、以太坊和莱特币。该方法整合了技术指标、Performer神经网络以及BiLSTM（双向长短期记忆网络），以捕捉时间动态并从原始加密货币数据中提取显著特征。技术指标的应用有助于提取复杂模式、动量、波动性和趋势。采用正交随机特征快速注意力机制（FAVOR+）的Performer神经网络，相较于传统Transformer模型中的多头注意力机制，展现出更高的计算效率和可扩展性。此外，在前馈网络中集成BiLSTM，通过从正向和反向两个方向处理数据，增强了模型捕捉时间动态的能力。这对于过去和未来数据点可能影响当前状态的时间序列数据尤为有利。所提出的方法已应用于主要加密货币的小时和日时间框架，其性能与文献中记载的其他方法进行了基准比较。结果凸显了该方法在超越现有模型方面的潜力，标志着加密货币价格预测领域的重要进展。