Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/59171
Title: Análise comparativa entre modelos de regressão distribucional e os principais algoritmos de aprendizado de máquina na predição de dados meteorológicos
Other Titles: Comparative analysis of distributional regression models and major machine learning algorithms for meteorological data prediction
Authors: Nakamura, Luiz Ricardo
Pereira, Geraldo Magela da Cruz
Guimarães, Paulo Henrique Sales
Oliveira, Tiago Almeida de
Ramires, Thiago Gentil
Keywords: Aprendizado de Máquina
Árvores Aleatórias
Aumento Extremo de Gradiente
The Generalized Additive Model for Location, Scale and Shape (GAMLSS)
Regressão por Vetores de Suporte
Extreme Gradient Boosting
Machine Learning
Random Forest
Support Vector Regression
Issue Date: 18-Jul-2024
Publisher: Universidade Federal de Lavras
Citation: SILVA, Viviane Costa. Análise comparativa entre modelos de regressão distribucional e os principais algoritmos de aprendizado de máquina na predição de dados meteorológicos. 2024. 91p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária)–Universidade Federal de Lavras, Lavras, 2024.
Abstract: Univariate regression models date back to the 19th century and aim to comprehend how a set of explanatory variables influences or explains a response variable. While it is common to encounter papers comparing flexible machine learning methodologies with conventional regression models, such a comparison may not be suitable due to the stringent assumptions and limited flexibility of typical regression models. Therefore, this dissertation proposes to assess and compare the performance of distributional regression models, initially proposed as generalised additive models for location, scale, and shape (GAMLSS), which represent a more modern and flexible approach, with other commonly employed machine learning algorithms in the literature, namely: random forest, support vector regression, extreme gradient boosting, and prophet, for meteorological datasets. In our first article, already published in a journal, the need to use GAMLSS in modelling daily average temperature over a one-year period in the city of Florianópolis, Brazil, was emphasized. This study demonstrated that less complex regression models would not be suitable for fully explaining the response due to the different regression structures built into its distribution. In the second paper, we compare the predictive performance of GAMLSS with the four other mentioned machine learning algorithms. We used data from an automatic weather station in the city of Florianópolis, Brazil, collected over 10 years (from 30 March 2013 to 28 March 2023). GAMLSS based on the Box-Cox t distribution returned more satisfactory results in most metrics used for comparing the fitted models, proving to be an interesting and robust alternative for fitting and predicting meteorological data.
URI: http://repositorio.ufla.br/jspui/handle/1/59171
Appears in Collections:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)



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