Enhancing Price Prediction in Cryptocurrency Using Transformer Neural
  Network and Technical Indicators
Mohammad Ali Labbaf Khaniki Mohammad Manthouri
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.