Cryptocurrency Price Prognosis using a Fused Deep Learning Model

Cryptocurrency is a major contributor to the modern digital era. Its markets have developed as a distinctive and extremely vibrant financial ecosystem, characterized by excessive instability and complexity. Cryptocurrency markets have been the centre of attention for all the stakeholders including investors, dealers, and researchers. The complex structure of this market is influenced by variables like technical indication and sentiment factors, hence making it difficult to achieve a high degree of accuracy. Precise price prediction is vital for investors and market participants, though it remains a formidable challenge. In this study, we apply a fused model of bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) neural networks to forecast the price of cryptocurrencies. This model makes use of the BiLSTM and BiGRU's bidirectional ability to capture complex temporal correlations and patterns in Litecoin, Monero, and Zcash price data, offering a detailed understanding of market dynamics.