Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China

Jie Zhu; Ruoling Zhang; Binbin Fu; Renhao Jin

doi:doi:10.11648/j.acm.20150406.19

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Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China

Jie Zhu, Ruoling Zhang, Binbin Fu, Renhao Jin

Published in Applied and Computational Mathematics (Volume 4, Issue 6)

Received: 1 November 2015 Accepted: 9 November 2015 Published: 19 November 2015

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Abstract

In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.

Published in	Applied and Computational Mathematics (Volume 4, Issue 6)
DOI	10.11648/j.acm.20150406.19
Page(s)	456-461
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Air Quality Index (AQI), Prediction, ARIMA Model, Exponential Smoothing Model, Holt Model

References

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[3]	Access:http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/jcgfffbz/201203/W020120410332725219541.pdf
[4]	Box, G.E.P., Jenkins, G.M., and Reinsel, G.C.(1994), Time Series Analysis: Forecasting and Control, 3^rd edition, Prentice Hall: Englewood Cliffs, New Jersey.
[5]	Box, G.E.P., and Pierce, D. (1970), “Distribution of Residual Autocorrelations in Auto-regressive-Intergrated Moving Average Time Series Models,” Journal of the American statistical Association, 65, 1509-1526.
[6]	Anders Milhoj (2013). Practical Time Series Analysis Using SAS. NC: SAS Institute Inc, Cary.
[7]	SAS Institute Inc, (2014). SAS/STAT® 9.4 User’s Guide: The ARIMA Procedure (Book Excerpt). NC: SAS Institute Inc, Cary.
[8]	SAS Institute Inc, (2014). SAS/STAT® 9.4 User’s Guide: The ESM Procedure (Book Excerpt). NC: SAS Institute Inc, Cary.
[9]	Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31 (3): 309-317.
[10]	Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation [J]. Econometric, 1982, 50 (4): 989-1004.
[11]	Engle R.F., Kroner F.K. Multivariate Simultaneous Generalized ARCH [J]. Econometric Theory, 1995, 11: 135-149.
[12]	Tsay, R.S., and Tiao, G.C. (1984), “Consistent Estimates of Auto-regressive Parameters and Extended Sample Auto-correlation Function for Stationary and Non-stationary ARMA models,” Journal of American Statistical Association, 79, 84-96.
[13]	Cox, D. R., & Wermuth, N. (1991). A simple approximation for bivariate and trivariate normal integrals. International Statistical Review/Revue Internationale de Statistique, 59(2), 263-269.
[14]	Engle Robert F. Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models [J]. Journal of Business and Economic Statistics, 2002, 20 (3): 341-347.
[15]	Engle R.F., Lilien D.M., Robins R.P. Estimating time-varying risk Premia in the term structure: The ARCH-M model [J]. Econometrica, 1987, 55: 395-406.
[16]	Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in B.N. Petrov and F. Csaki, ed. 2^nd International Symposium on Information Theory, 267-281. Akademia Kiado: Budapest.

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APA Style

Jie Zhu, Ruoling Zhang, Binbin Fu, Renhao Jin. (2015). Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Applied and Computational Mathematics, 4(6), 456-461. https://doi.org/10.11648/j.acm.20150406.19

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ACS Style

Jie Zhu; Ruoling Zhang; Binbin Fu; Renhao Jin. Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Appl. Comput. Math. 2015, 4(6), 456-461. doi: 10.11648/j.acm.20150406.19

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AMA Style

Jie Zhu, Ruoling Zhang, Binbin Fu, Renhao Jin. Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Appl Comput Math. 2015;4(6):456-461. doi: 10.11648/j.acm.20150406.19

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@article{10.11648/j.acm.20150406.19,
  author = {Jie Zhu and Ruoling Zhang and Binbin Fu and Renhao Jin},
  title = {Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {6},
  pages = {456-461},
  doi = {10.11648/j.acm.20150406.19},
  url = {https://doi.org/10.11648/j.acm.20150406.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150406.19},
  abstract = {In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.},
 year = {2015}
}

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TY  - JOUR
T1  - Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China
AU  - Jie Zhu
AU  - Ruoling Zhang
AU  - Binbin Fu
AU  - Renhao Jin
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PY  - 2015
N1  - https://doi.org/10.11648/j.acm.20150406.19
DO  - 10.11648/j.acm.20150406.19
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
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PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.acm.20150406.19
AB  - In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.
VL  - 4
IS  - 6
ER  -

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Author Information

Jie Zhu

School of Information, Beijing Wuzi University, Beijing, China
Ruoling Zhang

School of Information, Beijing Wuzi University, Beijing, China
Binbin Fu

School of Information, Beijing Wuzi University, Beijing, China
Renhao Jin

School of Information, Beijing Wuzi University, Beijing, China

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APA Style

Jie Zhu, Ruoling Zhang, Binbin Fu, Renhao Jin. (2015). Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Applied and Computational Mathematics, 4(6), 456-461. https://doi.org/10.11648/j.acm.20150406.19

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ACS Style

Jie Zhu; Ruoling Zhang; Binbin Fu; Renhao Jin. Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Appl. Comput. Math. 2015, 4(6), 456-461. doi: 10.11648/j.acm.20150406.19

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AMA Style

Jie Zhu, Ruoling Zhang, Binbin Fu, Renhao Jin. Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China. Appl Comput Math. 2015;4(6):456-461. doi: 10.11648/j.acm.20150406.19

Copy | Download

@article{10.11648/j.acm.20150406.19,
  author = {Jie Zhu and Ruoling Zhang and Binbin Fu and Renhao Jin},
  title = {Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {6},
  pages = {456-461},
  doi = {10.11648/j.acm.20150406.19},
  url = {https://doi.org/10.11648/j.acm.20150406.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20150406.19},
  abstract = {In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China
AU  - Jie Zhu
AU  - Ruoling Zhang
AU  - Binbin Fu
AU  - Renhao Jin
Y1  - 2015/11/19
PY  - 2015
N1  - https://doi.org/10.11648/j.acm.20150406.19
DO  - 10.11648/j.acm.20150406.19
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 456
EP  - 461
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20150406.19
AB  - In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.
VL  - 4
IS  - 6
ER  -

Copy | Download