Enhanced Forecasting of Air Passenger Trends: A Multi Component Time Series Approach Utilizing Seasonal Adjustments and Exogenous Variables

This study presents a comprehensive approach to enhancing air passenger forecasting using advanced time series analysis techniques. It begins with the systematic collection and preprocessing of historical passenger data, addressing missing values through mean imputation and linear interpolation, and detecting outliers using box plot analysis. Exploratory data visualization helps uncover hidden patterns and trends, while seasonality decomposition isolates trend, seasonal, and residual components, standardizing residuals for consistency. A structured train-test split forms the foundation for model evaluation, starting with baseline methods such as the naive, simple average, and moving average approaches, evaluated through RMSE and MAPE metrics. Forecast accuracy is further improved using exponential smoothing and the Holt-Winters method, which effectively capture both trends and seasonality. To ensure model reliability, stationarity is tested using the Augmented Dickey-Fuller and KPSS tests, with data transformations like Box-Cox and differencing applied where necessary. Autocorrelation and partial autocorrelation analyses guide parameter selection for ARIMA and SARIMA models, with SARIMAX offering enhanced seasonal modeling through external variable integration. The finalized models are trained and validated, demonstrating strong predictive performance and offering a reliable framework for forecasting air passenger volumes. This methodology not only improves forecast accuracy but also provides a scalable and adaptable model applicable to time series forecasting challenges in various domains.