Enhancing Stock Price Prediction Using Hybrid Auto Regressor and Machine Learning Models

The prediction or estimation of stock prices remains a complex and formidable task for humans, given the volatile price trends of stocks attributed to various factors. Although traditional methods, such as Fundamental Analysis and Technical Analysis, are in use, there is a pressing need to enhance these methods to safeguard funds from potential losses due to unavoidable factors in the stock market. To this end, we have proposed a novel method that predicts stock trends using an Auto Regressor machine learning model that leverages time series data. The suggested model presupposes that a time series' present value depends on its previous values., and the relationship between them is linear, with the strength between them being determined by an autoregressive coefficient.