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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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 |