Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

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 conc...

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Main Author: Ng, Kar Yong
Format: Thesis
Language:English
Published: 2017
Subjects:
QA1 Mathematics (General)
Online Access:http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf
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spelling my-usm-ep.478262020-10-28T07:46:54Z Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia 2017-11 Ng, Kar Yong QA1 Mathematics (General) 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. 2017-11 Thesis http://eprints.usm.my/47826/ http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Matematik (School of Mathematical Sciences)
institution Universiti Sains Malaysia
collection USM Institutional Repository
language English
topic QA1 Mathematics (General)
spellingShingle QA1 Mathematics (General)
Ng, Kar Yong
Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
description 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.
format Thesis
qualification_level Master's degree
author Ng, Kar Yong
author_facet Ng, Kar Yong
author_sort Ng, Kar Yong
title Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
title_short Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
title_full Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
title_fullStr Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
title_full_unstemmed Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
title_sort statistical modelling for forecasting pm10 concentrations in peninsular malaysia
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Sains Matematik (School of Mathematical Sciences)
publishDate 2017
url http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf
_version_ 1747821836799836160

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