Cryptocurrency price prediction through integrated forecasting techniques

In cryptocurrency, where market volatility and unpredictability pose challenges to investors and businesses alike, there is a growing need for more accurate price prediction methods. This research paper introduces an innovative approach aimed at enhancing cryptocurrency price prediction using deep learning techniques. Focusing on 20 prominent cryptocurrencies, this study employs Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (Bi-LSTM), Hybrid LSTM-GRU models to harness the power of deep learning. Through the utilization of these advanced models, improved accuracy in price prediction is demonstrated. Additionally, a strategy is proposed to guide users on whether to buy, sell, or hold cryptocurrencies daily, enhancing decision-making in volatile markets. Integration of advanced models and strategic insights aims to facilitate informed financial decisions and business strategies in the cryptocurrency market. Preprocessing of data using the Min-Max scaler method, a technique that effectively scales features within a predetermined range, aiding in the convergence and optimization of deep learning models. The integration of such preprocessing techniques, coupled with the inherent strength of LSTM and GRU models, culminates in a predictive framework that significantly surpasses conventional methods of cryptocurrency price prediction. By outperforming traditional approaches, this research contributes to the advancement of cryptocurrency prediction, thereby facilitating more informed financial decision-making and business strategies.