Forecasting Model of Air Pollution Index using Generalized Autoregressive Conditional Heteroskedasticity Family (GARCH)
DOI:
https://doi.org/10.11113/mjfas.v18n2.2279Keywords:
Forecast Time series, Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Air Pollution Index, Integer-Value.Abstract
The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroskedasticity (GARCH) family for discrete case namely poisson integer value GARCH (INGARCH), negative binomial integer value GARCH (NBINGARCH) and integer value autoregressive conditional heteroskedasticity (INARCH) models. The parameters of the models will be estimated using quasi likelihood estimator (QLE) and we compare their Akaike information criterion (AIC) to determine the best model fitted the data. The results showed that INGARCH (1,1) and INARCH (1,0) performed inconsistent results since the conventional methods of NBINGARCH (1,1) outperformed the performance of INGARCH (1,1) and INARCH (1,0). However, consistet results were achieved as the NBINGARCH (1,1) gave the smallest forecasting error compared to INGARCH (1,1) and INARCH (1,0). The findings are very important for controlling the API results in future and taking protection measure for conservation of the air.
References
J. K. Sethi and M. Mittal, “Analysis of air quality using univariate and multivariate time series models,” Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, pp. 823–827, 2020.
M. Kampa and E. Castanas, “Human health effects of air pollution.,” Environmental Pollution, vol. 151, no. 2, pp. 362–357, 2008.
A. B. Kurt, A., Oktay, “Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks,” Expert Systems with Applications, vol. 37, no. 12, pp. 7986–7992, 2010.
World Resources Institute, “World Resources Institute: (2002) Rising Energy Use: Health Effects of Air Pollution.,” World Resources Institute. http://www.airimpacts.org (2002)., vol. Accessed J, 2002.
X. Y. Ni, H. Huang, and D. W, P, “Relevance analysis and short-term prediction of PM 2.5 concentration in Beijing based on multisource data,” Atmospheric Environment, vol. 150, pp. 146–161, 2017.
Chelani, B. Asha, and S. Devotta, “Air quality forecasting using a hybrid autoregressive and nonlinear model,” Atmospheric Environment 40, vol. 10, pp. 1774–1780, 2006.
A. Kumar and P. Goyal, “Forecasting of daily air quality index in Delhi,” Science of the Total Environment, vol. 24, no. 409, pp. 5517–5523, 2011.
Tsakiri, K. G, and Z. Igor, G, “Prediction of ozone concentrations using atmospheric variables,” Air Quality, Atmosphere & Health, vol. 2, no. 4, pp. 111–120, 2011.
E. M. Y. Wu, S. L. Kuo, and W. C. Liu, “Generalized autoregressive conditional heteroskedastic model for water quality analyses and time series investigation in reservoir watersheds,” Environmental Engineering Science, vol. 29, pp. 227–237, 2012.
Kadiyala, Akhil, and A. Kumar, “Multivariate time series models for prediction of air quality inside a public transportation bus using available software,” Environmental Progress & Sustainable Energy, vol. 2, no. 33, pp. 337–341, 2014.
Passamani, Giuliana, and M. Paola, “Local atmospheric pollution evolution through time series analysis,” Journal of Mathematics and Statistical Science 2, vol. 12, pp. 781–788, 2016.
K. I. Hoi, K. V. Yuen, and K. M. Mok, “Prediction of daily averaged PM10 concentrations by statistical time-varying model,” Atmospheric Environment 43, vol. 16, pp. 2579–2581, 2009.
N. N. Mohamad, I. Mohamed, and N. K. Haur, “Moment properties and quadratic estimating functions for integer-valued time series models,” Pakistan Journal of Statistics and Operation Research, vol. 14, no. 1, pp. 157–175, 2018.
R. B. Silva and W. Barreto-Souza, “Flexible and Robust Mixed Poisson INGARCH Models,” Journal of Time Series Analysis, vol. 40, no. 5, pp. 788–814, 2019,
Q. Li, H. Chen, and F. Zhu, “Robust Estimation for Poisson Integer-Valued GARCH Models Using a New Hybrid Loss,” Journal of Systems Science and Complexity, vol. 34, no. 4, pp. 1578–1596, Aug. 2021.
B. Kim, S. Lee, and D. Kim, “Robust estimation for bivariate poisson ingarch models,” Entropy, vol. 23, no. 3, Mar. 2021.
World Air Quality Index, “World’s Air Pollution: Real-time Air Quality Index,” World Air Quality Index, 2020.
R. Ferland, A. Latour, and D. Oraichi, “Integer-valued GARCH process,” Journal of Time Series Analysis, vol. 27, no. 6, pp. 923–942, 2006.
C. Weiß, “Modelling time series of counts with overdispersion,” Statistical Methods and Application, vol. 18, pp. 507–519, 2009.
T. Liboschik, K. Fokianos, and R. Fried, “Tscount: An R package for analysis of count time series following generalized linear models,” Journal of Statistical Software, vol. 82, no. November, 2017.
P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, vol. 68, no. 22. 2016. doi: 10.1063/1.115817.
T. Stadnitski, “Time series analyses with psychometric data,” PLoS ONE, vol. 15, no. 4, pp. 1–12, 2020.
E. H. Glenn, “Getting Started in R,” pp. 9–14, 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Nurul Asyikin Zamrus, Mohd Hirzie Mohd Rodzhan, Nurul Najihah Mohamad
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
