Hybrid Model for Cryptocurrency Price Prediction using LSTM, Bidirectional LSTM, and XGBoost

Abstract

Guiding through the complicated and unstable setting of The rapid shifts in cryptocurrency markets urge a need for creative predictive techniques that surpass standard financial analysis instruments. This paper offers an innovative new hybrid forecasting model by combining the strong analytical properties of LSTM, Bi-LSTM, and XGBoost. This model takes advantage of LSTM networks to interpret temporal sequences. in historical pricing data, the Bidirectional LSTM layer improves. analysis that includes future data insights. XGBoost complements this frame-work through the systematic reduction of errors of prediction. by application of progressive ensemble learning techniques. The synergy of these techniques furnish a substantial method that is fit for capturing the the inconsistent behavior of cryptocurrency values and enhancing forecasting. accuracy. Comparing this hybrid strategy to empirical tests have shown that. An examination across historical data shows that traditional models exhibit major enhancements in precision of predictions, illustrating its practical benefit. for financial analysts and investors. The successful application of combining these sophisticated technologies denotes a serious progress in predictive methods for the intense and constantly changing sector of digital currencies.