Real-Time Data Extraction and Prediction of Cryptocurrency - Sanika Chavan; Jahnavi Gundakaram; Sai Dyuti Vaishnavi; Srishti Prasad; K. Deepa (IEEE Access)
Cryptocurrency markets exhibit high volatility, necessitating accurate forecasting methods for effective decision-making. This paper presents an innovative approach that integrates web scraping from cryptocurrency websites with various deep-learning networks to predict cryptocurrency values for the following day. Our web scraping technique integrated with concept like multi-threading focuses exclusively on cryptocurrency websites, extracting essential data such as live price records making use of crucial computer technology concepts like multi-threading. Combined with a suite of deep learning models including LSTM, GRU, and XgBoost, this data facilitates the modelling of temporal dependencies crucial for understanding cryptocurrency price dynamics. Through empirical evaluation, we determine the model that outperforms others and integrate it into a dashboard for real-time prediction. By leveraging real-time insights from web scraping, our model aims to enhance prediction accuracy. This research contributes to the advancement of predictive analytics in cryptocurrency trading, providing actionable insights for investors and analysts amidst fluctuating market conditions.