Open AccessAn Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction with Sentimental Cautioning
Author(s) -
Muhammad Zubair,
Jaffar Ali,
Musaed Alhussein,
Shoaib Hassan,
Khursheed Aurangzeb,
Muhammad Umair
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3367129
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Cryptocurrencies, recognized by their extreme volatility due to dependency on multiple direct and indirect factors, offer a significant challenge regarding precise price forecasting. This uncertainty has led to investment hesitation within the digital currency market. Previous research attempts have presented methodologies for price forecasting and trend prediction in cryptocurrencies. However, these forecasts have typically suffered from increased error rates, leaving the opportunity for improvement in this field. Furthermore, the influence of sentiment-based factors could compromise the reliability of price predictions. In this research, we have proposed a machine learning-driven framework that provides precise cryptocurrency price projections and adds an alert mechanism to guide investors. Our fundamental analyzer, Bi-LSTM and GRU hybrid model use historical data of digital currencies to train and reliably anticipate future values. Complementing this, a sentiment analyzer, utilizing a BERT and VADER hybrid model, analyzes sentiments to assess the forecast price as trustworthy or uncertain. Besides assisting investor decision-making, this technique also helps risk management in the dynamic realm of cryptocurrency. Our suggested approach delivers highly precise price predictions with dramatically decreased error rates compared to prior competitive studies. The proposed Bi-LSTM-GRU-BERT-VADER model is tested for three cryptocurrencies, namely BTC, ETH, and Dogecoin and reports an average root mean square error (RMSE) of 0.0241%, 0.0645%, and 0.0978%, respectively.