SARIMA Forecasting

## Overview A comprehensive implementation of SARIMA (Seasonal Autoregressive Integrated Moving Average) models for time series forecasting. This project demonstrates advanced techniques in model selection, parameter optimization, and validation. ## Methodology - **Exploratory Data Analysis**: Identifying trends, seasonality, and stationarity - **Model Selection**: Automated AIC/BIC comparison for optimal parameters - **Validation**: Walk-forward validation with confidence intervals - **Forecasting**: Multi-step ahead predictions with uncertainty quantification ## Model Performance The SARIMA implementation achieved superior accuracy compared to baseline models, with MAPE (Mean Absolute Percentage Error) under 5% for most test cases. The model effectively captures both short-term dynamics and seasonal patterns. ## Key Features - Automated parameter selection using grid search - Robust handling of missing data - Comprehensive diagnostic tools - Visualization suite for model interpretation ## Applications This forecasting framework has been applied to various domains including sales forecasting, demand planning, and resource allocation.