Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

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dc.contributor.authorNg, Kar Yong
dc.date.accessioned2020-10-28T04:14:34Z
dc.date.available2020-10-28T04:14:34Z
dc.date.issued2017-11
dc.description.abstractThis research aims to forecast the daily average PM10 concentrations in Peninsular Malaysia by using univariate modelling, i.e. time series modelling and regression modelling. In time series analysis, a typical problem in forecasting is the underestimation of the peaks. Since the series of PM10 concentrations change rapidly, this research proposed the use of wavelet-based time series model to improve the forecast accuracy, i.e. the application of discrete wavelet transform (DWT) before the time series modelling by the Box-Jenkins autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) models. By employing DWT, the volatile PM10 series were decomposed into several subsidiary series with smaller variations, and consequently, helped in improving the total forecast accuracy substantially especially during haze periods when compared to the time series modelling without DWT.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/10651
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectStatistical Modellingen_US
dc.subjectForecasting Pm10 Concentrationsen_US
dc.subjectPeninsular Malaysiaen_US
dc.titleStatistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysiaen_US
dc.typeThesisen_US
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