Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia

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dc.contributor.authorNg Kar Yong
dc.contributor.authorNorhashidah Awang
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:26Z
dc.date.available2024-05-10T03:25:26Z
dc.date.issued2019
dc.description.abstractThis study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy.The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT.
dc.identifier.citationYong, N. K., & Awang, N. (2019). Wavelet-based time series model to improve the forecast accuracy of PM 10 concentrations in Peninsular Malaysia. Environmental monitoring and assessment, 191, 1-12.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/560
dc.language.isoen
dc.publisherSpringer
dc.publisher.alternativeEnvironmental Monitoring and Assessment
dc.subjectARIMA-GARCH
dc.subjectDiscrete wavelet transform
dc.subjectForecast
dc.subjectParticulate matter
dc.subjectTime series
dc.titleWavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia
dc.typeJournal

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