Analyzing Cryptocurrency Price Trends for Real-Time Price Predictions

Abstract

Cryptocurrency has emerged as a popular investment option due to its decentralized nature and potential for high returns. However, the cryptocurrency market is characterized by high volatility and price fluctuations, making it difficult for investors and traders to make informed decisions. This paper aims to address this challenge by performing a time series analysis of cryptocurrency prices to estimate values in real-time. To achieve this goal, historical price data for three cryptocurrencies—Bitcoin, Ethereum, and Litecoin are gathered and preprocessed. Following this, a range of time series techniques are used to analyze the patterns and trends in the data. This study focuses on hourly and daily data of the cryptocurrencies and employs three hybrid models such as CNN-LSTM (CLT), CNN-GRU (CGR), and CNN-BiLSTM (CBL) to forecast upcoming prices. Among three models, the CLT technique outperforms other models with RMSE of 235.97, MAE of 135.42, and MAPE of 0.47% on hourly Bitcoin price prediction. The experimental results demonstrate the effectiveness of the proposed methods in predicting the prices of cryptocurrencies in real-time.