MATLAB Code Implementation for ARIMA Model

This MATLAB-based source code implements the ARIMA (AutoRegressive Integrated Moving Average) model, a powerful time series analysis framework widely used for forecasting applications. The implementation includes complete model building procedures featuring parameter estimation algorithms (using maximum likelihood or conditional sum of squares methods), differencing operations for stationarity, and spectral analysis components for frequency domain examination. The code structure allows users to examine detailed implementation aspects including autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis for order identification, model fitting routines, and forecast error calculation mechanisms. Configurable parameters enable customization of AR (autoregressive), I (integration), and MA (moving average) orders, along with optimization settings for convergence criteria and maximum iteration counts. Additional features include residual diagnostics, model validation checks, and visualization tools for time series decomposition. This implementation serves as a valuable resource for understanding ARIMA model mechanics while providing robust forecasting capabilities through adjustable parameters that enhance prediction accuracy based on specific dataset characteristics. The comprehensive nature of this tool offers researchers and practitioners advanced time series analysis and prediction functionality with professional-grade implementation standards.