Wavelet-based time series model to improve the forecast accuracy of PM concentrations in Peninsular Malaysia.

This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series.

Multiple linear regression and regression with time series error models in forecasting PM concentrations in Peninsular Malaysia.

Frequent haze occurrences in Malaysia have made the management of PM (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM variation and good forecast of PM concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM concentrations based on predictor variables including meteorological parameters and gaseous pollutants.