MATLAB Implementation of ARMA Model

This MATLAB code implements the AutoRegressive Moving Average (ARMA) model, a fundamental time series forecasting method for predicting future data points. The implementation includes core functionalities such as parameter estimation using maximum likelihood methods, model order selection criteria (AIC/BIC), and forecast generation algorithms. The code is thoroughly commented to explain key computational steps, including the handling of autoregressive (AR) and moving average (MA) components, residual calculations, and model diagnostics. Each section demonstrates practical implementation aspects like the use of MATLAB's System Identification Toolbox functions (e.g., armax) or custom implementations of the Yule-Walker equations. Researchers and practitioners can utilize this annotated codebase to understand ARMA model mechanics, conduct experiments, and extend it for advanced time series analysis applications. The implementation serves as an educational foundation for developing customized forecasting solutions and exploring related models like ARIMA and SARIMA.