Previsão do índice de desenvolvimento humano de 2013 e 2014 por meio de técnicas de mineração de dados em séries temporais univariadas e multivariadas
| Autor: | Dos Santos, Celso Bilynkievycz, Pedroso, Bruno, Guimarães, Alaine Margarete [UNESP], Pilatti, Luiz Alberto, Kovaleski, João Luiz |
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| Přispěvatelé: | Universidade Estadual de Ponta Grossa (UEPG), Universidade Estadual de Campinas (UNICAMP), Universidade Estadual Paulista (Unesp), UTFPR, Université Joseph Fourier |
| Jazyk: | portugalština |
| Rok vydání: | 2019 |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| Popis: | Made available in DSpace on 2020-12-12T01:07:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 The Human Development Index (HDI) is an indicator adopted by the World Health Organization to assess the quality of life of a given region. Its prediction can aid in planning and decision-making for policy guidance and advocacy to improve its development. This study predicted the HDI of 2013 and 2014 from forecasting data mining techniques in time series, completing all stages of the knowledge discovery process in databases. In the study, the predictive capacity of 376 models, two generic and 374 country specific, were evaluated. For the development of the models we used the SMOReg algorithm, executed in a Forecast programming interface application of the WEKA environment. The generic model was trained and tested with multivariate time series corresponding to the HDI records of 187 countries, while the specific models were developed from univariate time series corresponding to the individual historical behavior of the index in each country. The time variables used corresponded to historical and intermittent periods from 1980 to 2013 published in the report of the United Nations Development Program on 07/24/2014. In the empirical analysis it was verified that the multivariate models presented the best quality measures in the predictions. The predictions of the HDI 2013 were efficient, with no significant differences to published figures, while the predictions of HDI 2014 depend on comparison with figures released after the completion of the present study. Setor de Ciências Biológicas e da Saúde UEPG., Av. General Carlos Cavalcanti, 4748. Uvaranas Universidade Estadual de Campinas (Unicamp) Universidade Estadual Paulista Júlio de Mesquita Filho UNESP UTFPR Université Joseph Fourier Universidade Estadual Paulista Júlio de Mesquita Filho UNESP |
| Databáze: | OpenAIRE |
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