An Enhanced Prediction Logic to Forecast Bitcoin Price Variations Using Elevated Machine Learning Principles

The volatility of Bitcoin prices presents both challenges and opportunities for investors and researchers seeking to understand and forecast cryptocurrency market dynamics. In this study, we propose a Hybrid Recurrent Neural Network and Autoregressive Integrated Moving Average (RNN-ARIMA) model to predict Bitcoin price variations. Leveraging minute-to-minute Bitcoin price data sourced from Bitstamp exchange, our methodology integrates the capabilities of RNNs and ARIMA models to capture both temporal dependencies and linear trends in the data. Through rigorous experimentation, we evaluate the performance of the hybrid model against baseline models and alternative configurations. Our results demonstrate the superiority of the Hybrid RNN-ARIMA model, with a Mean Absolute Error (MAE) of 120.54, Mean Squared Error (MSE) of 25098.23, Root Mean Squared Error (RMSE) of 158.45, R-squared (R2) of 0.87, and Mean Absolute Percentage Error (MAPE) of 3.8%. These findings underscore the effectiveness of the hybrid modeling approach in accurately predicting Bitcoin price variations and provide valuable insights for stakeholders navigating the volatile cryptocurrency market.