  For a long-time, researchers have been developing a reliable and accurate
predictive model for stock price prediction. According to the literature, if
predictive models are correctly designed and refined, they can painstakingly
and faithfully estimate future stock values. This paper demonstrates a set of
time series, econometric, and various learning-based models for stock price
prediction. The data of Infosys, ICICI, and SUN PHARMA from the period of
January 2004 to December 2019 was used here for training and testing the models
to know which model performs best in which sector. One time series model
(Holt-Winters Exponential Smoothing), one econometric model (ARIMA), two
machine Learning models (Random Forest and MARS), and two deep learning-based
models (simple RNN and LSTM) have been included in this paper. MARS has been
proved to be the best performing machine learning model, while LSTM has proved
to be the best performing deep learning model. But overall, for all three
sectors - IT (on Infosys data), Banking (on ICICI data), and Health (on SUN
PHARMA data), MARS has proved to be the best performing model in sales
forecasting.
