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

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.

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