Abstract
The paper show the application of the ARIMA (Autoregressive integrated moving average) prediction model is made, which consists of the use of statistical data (in this case, birth and deaths in Colombia) to formulate a system in which an approximation of future data is obtained, this thanks to the help of a statistical software that allows us to interact with the variables of this model to observe a behavior as accurately as possible, the research source was extracted from the statistical data provided by the national statistical department, articles about cases in the that this method was used, and statistical texts. The development of the research was carried out observing the statistics provided by national statistical department, creating a database of births and deaths in Colombia per year, taking into account total figures at the national and departmental levels. Thanks to these data, a tool such as software and prior knowledge of the predictive model ARIMA (Autoregressive integrated moving average) is achieved to make an approximate prediction of what could happen in a given time, thus taking the measures required by each department, solving possible problems in the country.
Supported by Universidad Cooperativa de Colombia.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
de la Republica de Colombia, P.: Dirección Nacional de Estadística. http://www.dane.gov.co/
Cortes, F., et al.: Time series analysis of dengue surveillance data in two Brazilian cities. Acta Tropica 182, 190–197 (2018). https://doi.org/10.1016/j.actatropica.2018.03.006
Brillinger, D.R.: Time Series - an overview – ScienceDirect Topics. Elsevier (2001). https://www.sciencedirect.com/topics/economics-econometrics-and-finance/time-series
He, Z., Tao, H.: Epidemiology and ARIMA model of positive-rate of influenzaviruses among children in Wuhan, China: a nine-year retrospectivestudy. Int. J. Infect. Dis. 74, 61–70 (2018). https://doi.org/10.1016/j.ijid.2018.07.003. www.sciencedirect.com/science/article/pii/S1201971218344618
Moon, T., Shin, D.H.: Forecasting construction cost index using interrupted time-series. KSCE J. Civil Eng. 22(5), 1626–1633 (2018). https://doi.org/10.1007/s12205-017-0452-x
Nury, A.H., Hasan, K., Alam, M.J.B.: Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. J. King Saud Univ. - Sci. 29(1), 47–61 (2017). https://doi.org/10.1016/j.jksus.2015.12.002
Torbat, S., Khashei, M., Bijari, M.: A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets. Econ. Anal. Policy 58, 22–31 (2018). https://doi.org/10.1016/j.eap.2017.12.003. http://www.sciencedirect.com/science/article/pii/S031359261730067X
Wang, C.C.: A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export. Exp. Syst. Appl. 38(8), 9296–9304 (2011). https://doi.org/10.1016/j.eswa.2011.01.015. http://www.sciencedirect.com/science/article/pii/S0957417411000352
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lancheros-Cuesta, D., Bermudez, C.D., Bermudez, S., Marulanda, G. (2020). Predictive Model of Births and Deaths with ARIMA. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12013. Springer, Cham. https://doi.org/10.1007/978-3-030-45093-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-45093-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45092-2
Online ISBN: 978-3-030-45093-9
eBook Packages: Computer ScienceComputer Science (R0)