A Cryptocurrency Price Prediction Model using Deep Learning

Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency is a difficult task due complex nature the absence of central authority. In this paper, our proposal employ Long Short-Term Memory (LSTM) networks, type deep learning technique forecast cryptocurrencies. We use historical price data technical indicators inputs LSTM model, which learns underlying patterns trends data. To improve accuracy predictions, we also incorporate Change Point Detection (CPD) using Pruned Exact Linear Time (PELT) algorithm. This method allows us detect significant changes adjust model accordingly, leading better predictions. evaluate approach predominantly on Bitcoin cryptocurrency, but can implemented other cryptocurrencies provided there valid Our experimental results show that proposed outperforms baseline algorithm, achieving higher performance terms Mean Absolute Error (MAE), Square (MSE), Root (RMSE). research findings suggest combining techniques such with change point detection PELT prediction practical implications for investors, traders, financial analysts.