Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/58260
Title: Previsão de séries temporais com máquinas de suporte vetorial
Other Titles: Time series forecast with vector support machines
Authors: Guimarães, Paulo Henrique Sales
Sáfadi, Thelma
Pereira, Geraldo Magela da Cruz
Pereira, Tiago Martins
Keywords: Análise de componentes principais
Análise de componentes independentes
Análise técnica
Principal component analysis
Independent components analysis
Technical analysis
Support vector machine
Issue Date: 11-Aug-2023
Publisher: Universidade Federal de Lavras
Citation: MARTINS, R. A. Previsão de séries temporais com máquinas de suporte vetorial. 2023. 62 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária)–Universidade Federal de Lavras, Lavras, 2023.
Abstract: This dissertation uses the Support Vector Machine (SVM) technique combining Principal Components and Independent Components analysis in the evaluation of financial time series. This subject is of great interest to researchers, investors and financial institutions that seek to understand the behavior/influence on decision-making in the price market. It is known that the combination of Principal and Independent Components analysis, together with vector support machines can guarantee better results for the context. As a result, it appears that the PCA - SVR, ICA - SV models showed better accuracy when compared to common models, such as the SVR simply. The results of the MAE, MSE, RMSE, R 2 metrics corroborate the applied models in question.
URI: http://repositorio.ufla.br/jspui/handle/1/58260
Appears in Collections:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)

Files in This Item:
File Description SizeFormat 
DISSERTAÇÃO_Previsão de séries temporais com máquinas de suporte vetorial.pdf787,47 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons