Stock Price Prediction using Hybrid Deep Learning Technique for Accurate Performance

Abstract

Deep learning and intelligent systems are constantly growing in popularity in the modern world. Artificial intelligence has several uses, all of which relate to human activities. Projection analysis is one of the general uses of neural networks and artificial intelligence. The authors of this work also carried out an artificial intelligence-based comparison investigation. Using various models, authors have made stock market predictions. Since stock markets are inherently unpredictable, accurate prediction analysis is crucial for assessing stock values and their downs and ups throughout time. Using algorithms for machine learning on data from financial news, which can also modify investors' interests, the stock values can be readily anticipated. Traditional prediction techniques, on the other hand, are no longer effective when applied to non-stationary time series information. With the development of deep learning technologies, this research suggests a way for accurately predicting stock prices.