Parsimonious time series models on air pollution index in Malaysia: A comparative study

A statistical modeling of air pollution index data can provide useful information about the characteristics and behaviors of air pollution events. This study evaluates the performance of the three parsimonious time series model on API data in Malaysia, which are: autoregressive integrated moving ave...

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Bibliographic Details
Main Authors: Heng, Loh Jun, Masseran, Nurulkamal
Format: Conference Proceeding
Language:English
Subjects:
Air pollution
Artificial neural networks
Autoregressive models
Comparative studies
Fuzzy logic
Performance evaluation
Statistical analysis
Statistical models
Time series
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Summary:A statistical modeling of air pollution index data can provide useful information about the characteristics and behaviors of air pollution events. This study evaluates the performance of the three parsimonious time series model on API data in Malaysia, which are: autoregressive integrated moving average, seasonal autoregressive integrated moving average, fuzzy time series, and artificial neural networks. A case study involving the analysis and modeling of API data for 16 locations in Malaysia is conducted. Results show that the API values fall between good and moderate levels. Some large fluctuations of the APIs are found to be mainly due to haze events. Modeling results indicate that the ARIMA method is highly applicable to the accurate prediction of API values in most cases. Nevertheless, the other forecasting methods could serve as good alternatives because of their similar performance to the ARIMA method.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0228002