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A Descriptive Time Series Analysis Applied to the Fit of Carbon-Dioxide (CO2)

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The study examined the use of population spectrum in determining the nature (deterministic and stochastic) of trend and seasonal component of given time series. It also adopts the use of coefficient of variation approach in the choice of appropriate model in descriptive time series technique. Illustrations were carried out using average monthly atmospheric Carbon dioxide (C02) from 2000-2017 with 2018 used for forecast. Spectrum analysis showed that the descriptive technique of time series is more appropriate for analysis of the study data. The coefficient of variation revealed that the multiplicative model was appropriate for the CO2 data while the forecast and the actual values showed no significant mean difference at 5% level of significance.


Bulletin of Mathematical Sciences and Applications (Volume 21)
C. J. Nweke et al., "A Descriptive Time Series Analysis Applied to the Fit of Carbon-Dioxide (CO2)", Bulletin of Mathematical Sciences and Applications, Vol. 21, pp. 1-8, 2019
Online since:
December 2019

[1] D.S. MacCarthy, R.B. Zougmore, P.B.I. Akponikpe, E. Koomson, P. Savadogo, and S.G.K. Adiku, Assessment of Greenhouse Gas Emissions from different Land-Use Systems: A case study of CO2 in the Southern Zone of Ghana. Applied and Environmental Soil Science, (2018).


[2] K.L. Ebi and L.H. Ziska, Increase in atmospheric carbon dioxide: Anticipated negative effects on food quality. PLoS Med, 7(2018),


[3] P. Wei and H. Pan, Research on individual carbon dioxide emissions of commuting in peri-urban area of metropolitan cities – an empirical study in Shanghai. Transportation Research Procedia, 25(2017), 3459-3478.


[4] X-L. Yue and Q-X. Gao, Contributions of natural systems and human activity to greenhouse gas emissions. Advances in Climate Change Research, 9(2018), 243-252.

[5] D. Huising, Z. Zhang, J.C. Moore, Q. Qao, and Q. Li, Recent Advances in Carbon Emissions Reduction: Policies, Technologies, Monitoring, Assessment and Modeling. Journal of Cleaner Production, (2015).


[6] R.B. Jackson, C.Le. Quere, R.M. Andrew, J.G. Canadell, G.P. Peters, J. Roy, and L. Wu, Warning signs of stabilizing global CO2 emissions. Environmental Research Letters, 12(2017), 1-12.


[7] W.C. Juang, S.J. Huang, F.D. Huang, P.W. Chang, and S.R. Wann, Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in southern Taiwan. BMJ Open, 11(2017),


[8] A. Shrivani, F. Moradi, and A.A. Moosavi, Time series modelling of increased soil temperature anomalies during long period. Int. Agrophys, 29(2015), 509-515.


[9] R. Paulo, L.R.S. Fernando, and O.P. Edson, Arima: an applied time series forecasting model for the bovespa stock index. Applied Mathematics, 5(2014), 3383-3391.


[10] I.S. Iwueze, E.C. Nwogu, J. Ohakwe, J. C. Ajaraogu,Uses of the Buys-Ballot Table in Time Series Analysis. Applied Mathematics, 2(2011), 633-645.


[11] C. Chatfield,The analysis of time series: An Introduction,(6th Edition), Chapman &Hall/CRC Press Company Boca, New York, (2004).

[12] G.E.P. Box, G.M. Jenkins, and G.C. Reinsel,Time Series Analysis: Forecasting and Control (3rd Edition). Prentice-Hall, Inc, Englewood Cliffs, (1994).

[13] V.S. Maxim, B, Adriaan, L.S. Nataliya, P.T. Anton, A.J. Timur, and K. Valerity, A Survey of Forecast Error Measures. World Applied Science Journal, 24 (2013), 171-176.

[14] J. Puerto and M.P. Rivera, Descriptive Analysis of Time Series Applied to housing Prices in Spain. 94342-CP-l-2001-De-Comentus-C21, (2001).

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