A Cryptocurrency Price Prediction Model using Deep Learning

Abstract

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

Keywords

• cryptocurrency
• change point detection algorithms
• bitcoin
• long short-term memory
• time-series data