A time series analysis-based stock price prediction using machine learning and deep learning models

Prediction of future movement of stock prices has always been a challenging task for researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have demonstrated that time series of a stock price can be predicted with a high level of accuracy. In this paper, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning, and deep learning models. We use daily stock price data, collected at five minutes intervals of time, of a very well-known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building the forecasting models. Extensive results have been presented on the performance of these models.