Cryptocurrency Analysis Using LSTM

Abstract

The high volatility and complexity of cryptocurrency markets create difficulties for investors and researchers who attempt to make accurate price predictions. This project presents an intelligent and data-driven approach for cryptocurrency analysis using Long ShortTerm Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN) which enables the model to learn temporal dependencies across sequential data. The system builds predictive models through training which uses historical price data that includes opening price, closing price, high, low, and trading volume. The proposed model follows a structured workflow that includes data collection from financial APIs, preprocessing techniques such as normalization and time-series windowing, and model training using LSTM architecture. The LSTM model enables accurate predictions through its ability to learn long-term dependencies and patterns which exist in cryptocurrency price movements. The evaluation process uses Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics to test prediction accuracy. The system includes a core function which displays actual versus predicted price data through graphical visualizations to assist users in understanding market patterns and model effectiveness. The approach provides better interpretability which helps traders and analysts to make better decisions. The system supports scalability which allows its application to various cryptocurrencies including Bitcoin and Ethereum and other digital currencies.