I. Introduction
Stock price prediction is a common topic in the domains of trade, finance, statistics, and computer science [1]. The stochastic character of the stock and the randomness of its behaviour make forecasting stock price difficult, and time series analysis of data appears to be ineffectual in capturing discontinuities, non-linearity, and high dataset complexity [2]. Statistical methods are used to predict the stock price at an early stage. Hence, traditional statistical methods cannot be employed to solve the problem of identifying the complexities of the stock market businesses [3, 4]. In order to overcome the shortcomings of above described statistical methods, stock market analysts and research investors have investigated on various machine learning (ML) techniques for stock prediction and making various trading decisions. There are many ML methods are employed for replacing the conventional statistical methods [5, 6].