Stock Market Forecasting Using Time Series Models – Project Description

Overview:
Stock Market Forecasting Using Time Series Models is an advanced project that applies statistical and machine learning techniques to predict stock market trends. It performs a comparative study of four key models: Simple Moving Average (SMA), Exponential Moving Average (EMA), Linear Regression (LR), and ARIMA. The project enables students to explore data collection, time series analysis, and predictive modeling in a practical financial context.

Suitable For:
·         BS Mathematics – applied time series analysis and modeling

·         BS Statistics – statistical forecasting and model evaluation

Technologies Used:

• Programming Language: Python

• Libraries & Tools: Pandas, NumPy, Matplotlib, Statsmodels, Scikit-learn

• Techniques: SMA, EMA, Linear Regression, ARIMA

• Visualization: Charts, trend lines, and comparative performance graphs

Features:

• Stock price prediction using multiple time series models

• Comparison of models based on accuracy and performance

• Graphical visualization of stock trends and forecasts

• Well-documented Python scripts with step-by-step explanations

• Ready-to-use datasets for practice and experimentation

Deliverables:

• Complete project source code (Python scripts)

• Project Documentation: Setup guide, code walkthrough, and detailed methodology

• Presentation Slides (PPT): Covering objectives, techniques, and results

• Banners / Visual Assets: Charts and graphics for showcasing the project

• Support: Assistance in running, customizing, or extending the project

Benefits for Students:

• Hands-on experience with time series forecasting and financial modeling

• Learn to implement and compare statistical and machine learning models

• Gain data visualization and analysis skills using Python

• Easily demoable for academic or professional presentations

• Strong addition to portfolios, capstone showcases, or financial data projects