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...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2017
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/47826/ http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf |
| _version_ | 1848880985826918400 |
|---|---|
| author | Ng, Kar Yong |
| author_facet | Ng, Kar Yong |
| author_sort | Ng, Kar Yong |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T18:11:50Z |
| format | Thesis |
| id | usm-47826 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:11:50Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-478262020-10-28T07:46:54Z http://eprints.usm.my/47826/ Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia 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 NonPeerReviewed application/pdf en http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf Ng, Kar Yong (2017) Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia. Masters thesis, Universiti Sains Malaysia. |
| spellingShingle | QA1 Mathematics (General) Ng, Kar Yong Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia |
| title | 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_short | Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia |
| title_sort | statistical modelling for forecasting pm10 concentrations in peninsular malaysia |
| topic | QA1 Mathematics (General) |
| url | http://eprints.usm.my/47826/ http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf |