Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

Ng, Kar Yong (2017) Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia. Masters thesis, Universiti Sains Malaysia.

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Abstract

This 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.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > QA1 Mathematics (General)
Divisions:	Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis
Depositing User:	Mr Aizat Asmawi Abdul Rahim
Date Deposited:	28 Oct 2020 07:46
Last Modified:	28 Oct 2020 07:46
URI:	http://eprints.usm.my/id/eprint/47826

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