Forecasting techniques for air pollution: utilizing Exponential Smoothing and ARIMA methods / Mohammad Nuzul Hakimi Mat Zain

Air pollution poses a significant threat to public health and the environment, necessitating the development of effective forecasting techniques to aid in pollution management and mitigation efforts. This study conducts a comparative analysis of two prominent time series forecasting methods, namely Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA), to predict air pollution levels. The primary objective is to evaluate the performance and accuracy of these methods in capturing the dynamic and complex patterns inherent in air quality data.The findings reveal that, in this specific context, the Exponential Smoothing method, particularly Holt-Winters, consistently demonstrates a lower Root Mean Squared Error (RMSE) compared to ARIMA. This suggests that Holt-Winters Exponential Smoothing provides more accurate predictions for air pollution levels.