Forecasting the Future: A Practical Approach to Stock Price Prediction Using Time Series and Machine Learning Models

Gouri J, Jogimol Joseph

Abstract: Time Series Forecasting is a method used to analyse data points collected at regular intervals for identifying trends, seasonality and future prediction. In machine learning, Time Series Forecasting is used to make future predictions based on historical trends. In financial sector, time series forecasting plays a crucial role in creating accurate financial predictions. By using historical data and economic indicators, companies can estimate future revenues, expenses, and cash flows, enabling informed decision-making in budgeting, investment, and loan assessments. Beyond prediction, the system provides company reviews based on forecasted stock performance, considering factors like price stability, growth potential, and prediction market trends. This project leverages time series forecasting to predict future stock prices using advanced models such as Machine learning models, Logistic Regression, and Random Forest.

Keywords: Stock market, Market trends, Logistic Regression, Random Forest, Time Series