Forecasting of Human Development Index of Latin American Countries Through Data Mining Techniques

Aim: Predict the Human Development Index (HDI) of 2013 and 2014 of Latin American countries through forecast data mining techniques. Methodology: Full stages of Knowledge Discovery in Databases applied in univariate and multivariate time series. For the prediction, the predicting abilities of 90 predicting models were tested, distributed in two global multivariate, 44 specific multivariate per country and 44 univariate. The algorithm SMOReg was adopted in the development of models as it presented a better performance among the learning algorithms based on functions tested in the experiment. Results: It was observed that the predictions of the models did not present significant statistical differences from the HDI tendencies disclosed in the last report of the United Nations Development Program. Nevertheless, the global multivariate models presented better quality measures in the predictions. Conclusion: The HDI prediction models used with multivariate time series provide better learning of algorithms with the increase of different univariate historical experiences.