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Soft Computing
Erschienen in: Soft Computing 14/2017

25.01.2016 | Methodologies and Application

Data analysis and statistical estimation for time series: improving presentation and interpretation

verfasst von: Cristina Şerbănescu, Cosmina-Elena Pop

Erschienen in: Soft Computing | Ausgabe 14/2017

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Abstract

In our days in the social sciences, time series (or longitudinal data) are ubiquitous, used in any analytic process, with the main scope to estimate or predict the future. The main issues are represented by the large variety of time series (sometime with an unknown size), the identification of outliers, and by the impossibility to estimate the error or numerical stability of statistical analysis. This paper proposed a matrix-based model for predictive analytics and, using a statistical estimation for different finite samples extracted from time series, estimated the residual and factorial variance for a group of samples. The proposed methods are applied on different samples of social data: number of births in a community, number of inhabitants, natural mobility of population, life expectancy (by sex and area), life expectancy at birth, fertility rate, infant mortality rate.
Vorheriger Artikel An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism
Nächster Artikel An online and incremental GRLVQ algorithm for prototype generation based on granular computing

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Metadaten
Titel
Data analysis and statistical estimation for time series: improving presentation and interpretation
verfasst von
Cristina Şerbănescu
Cosmina-Elena Pop
Publikationsdatum
25.01.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 14/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2041-1

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