Bitcoin price prediction using LSTM Algorithms

Abstract

The market’s intrinsic volatility and the influence of outside variables like investor emotion and economic indicators make it difficult to predict cryptocurrency prices. This study investigates several data mining techniques to raise the predicting accuracy of bitcoin prices. We use sentiment analysis, machine learning methods, and time series analysis in combination to model price movements more effectively. Traditional forecasting methods, including ARIMA and GARCH, are used alongside advanced neural networks and ensemble methods to capture complex, non-linear patterns in the data. Additionally, the study highlights the critical role of feature engineering and the application of clustering strategies to enhance predictive model performance.The integration of these approaches demonstrates a marked improvement in forecasting outcomes, providing not only more accurate price predictions but also valuable insights into market dynamics. These findings can assist investors in developing more robust investment strategies, allowing them to better navigate the cryptocurrency market’s risks. Our findings highlight how hybrid models can improve predictive capabilities and understanding market behavior in the evolving landscape of digital assets.