Enhancing Predictive Accuracy for Real Time Cryptocurrency Market Prices with Machine Learning Techniques

Cryptocurrency markets' extreme volatility demands advanced predictive models. The proposed neural network approach utilizes extensive research and real-image exchange data, revealing the Digital Internet of Things' impact. Addressing consumer influence on prices is vital. Our frame working corporate “doesn't make sense. It should probably be “Our framework incorporates. At the core is an enhanced deep learning framework, integrating autoregressive integrated moving average (ARIMA) with convolutional neural networks (CNNs). This synergy captures intricate price patterns. We integrate sentiment analysis from various sources and block chain data for a holistic market view. Model robustness is bolstered with hyper parameter optimization and cross-validation. Real-time data integration ensures timely predictions. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics are used in performance evaluation. Empirical evidence high lights our model's superiority in predicting cryptocurrency market variations. Compared to traditional methods like ARIMA, it offers substantial improvements, benefiting traders, investors, and decision-makers. Future enhancements include ensemble models, hyper parameter tuning, advanced deep learning, realtime data integration, and model interpretability, empowering stakeholders with precise insights into evolving cryptocurrency markets.