Forecasting Long Memory in Ozone Levels in Peninsular Malaysia using ARFIMA Model

Forecasting air pollutant concentrations is crucial for managing pollution levels that pose significant risks to human health and the environment. Among the various pollutants, ground-level ozone (O₃) is a secondary pollutant of particular concern due to its harmful health effects and complex formation dynamics involving nitrogen oxides (NOₓ)and volatile organic compounds (VOCs) under sunlight. This study focuses on predicting ozone levels using the autoregressive fractionally integrated moving average (ARFIMA) model, which effectively captures long memory behavior in time series data. The research begins by analyzing ozone concentration data from three monitoring stations across Peninsular Malaysia to identify long memory behavior. Using the Aggregated Variance (V/S) method, the presence of long memory is confirmed through the estimated differencing parameter d, which falls between 0 and 0.5 based on the Geweke and Porter-Hudak (GPH) estimator. The Augmented Dickey-Fuller (ADF) test was employed to assess the stationarity of the time series. Optimal ARFIMA models were selected based on the Akaike Information Criterion (AIC), and model performance was evaluated using the Root Mean