Unveiling Cryptocurrency Markets: A Comprehensive Exploration of Time Series and Deep Learning Models for Bitcoin Price Prediction

The volatility and dynamic nature of Bitcoin prices pose significant challenges for investors and analysts seeking to make informed decisions. In this paper, we explore the effectiveness of various time series forecasting models to predict Bitcoin prices. The models under consideration include traditional approaches such as ARIMA and ETS, machine learning methods like Random Forest and XGBoost, and advanced deep learning models including LSTM and GRU. This paper involves thorough data preparation and feature engineering, including the creation of technical indicators and statistical features to capture essential market dynamics. Each model is trained on historical Bitcoin price data and evaluated based on performance metrics such as Root Mean Squared Error (RMSE). Visualizations of actual versus predicted values provide insights into the models’ abilities to capture intricate price trends and fluctuations.