Comprehensive Prediction of Stock Prices Using Time Series, Statistical,
Machine Learning, and Deep Learning Models
- Jaydip Sen ,
- Abhishek Kumar ,
- Aji Thomas ,
- Nishant Kumar Todi ,
- Olive Olemmyan ,
- Swapnil Tripathi ,
- Vaibhav Arora
Abstract
Over the years, researchers have strived to develop reliable and
accurate predictive models for stock price prediction. The literature
suggests that well-designed and refined predictive models can provide
painstakingly precise estimates of future stock values. This project
aims to showcase a comprehensive set of models for predicting stock
prices, including time series, econometric, statistical, and machine
learning-based approaches. The dataset includes ten industry leaders
from different sectors and NIFTY50, spanning from January 2017 to
December 2022.
The models' performance was evaluated to determine which approach
performs best for different sectors. The time series models employed
include Holt's Linear Trend and Holt-Winters Exponential Smoothing,
while the econometric model utilized is ARIMA. Additionally, the
statistical model adopted is OLS, while several machine learning and
deep learning models incorporated a range of techniques such as Random
Forest, KNN, CNN, LSTM, etc. Besides predicting stock prices, the models
were also designed to determine the direction of stock price movements.
This project aims to provide insights into the best methods for
predicting stock prices and direction across different industries by
combining different models and approaches.