Vega-Cadillo, Claudio Andres

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Empirical comparison between multiple time series and functional data analysis
(2021-05-12) Vega-Cadillo, Claudio Andres; Acuña-Fernández, Edgar; College of Arts and Sciences - Sciences; Rolke, Wolfgang A.; Lorenzo-González, Edgardo; Department of Mathematics; Alers-Valentín, Hilton
Despite Functional data analysis being a competitor of time series analysis, it has been found that in the last 15 years it has not been requested as much as multiple time series by the scientific community. The main goal of this thesis is to study the similarities and differences between multiple time series analysis and functional data analysis. This was done by comparing the results of four main tasks: "Principal Component Analysis", "Outlier Detection", "Cluster Analysis" and "Supervised Classification" for both approaches in five real world datasets. After the experiments, it was found that for detecting outliers and principal components the Functional Data approach was superior. For clustering and classification, the Multiple Time Series approach performed slightly better, even though the methods were slower. Finally, Functional Data may not be as popular as Multiple time series, but it has been showed that it gives better results in terms on general analysis for one dimensional data.