Unsupervised learning on functional data with an application to the analysis of U.S. temperature prediction accuracy

Author: 
Date created: 
2019-02-07
Identifier: 
etd20091
Keywords: 
Unsupervised Learning
Functional Data Analysis
Unsupervised Random Forest
Functional Principal Component Analysis
Gaussian Mixture Model-based Clustering
Weather Forecast Exploratory Analysis
Abstract: 

Unsupervised learning techniques are widely applied in exploratory analysis as the motivation of further analysis. In functional data analysis, two typical topics of unsupervised learning are functional principal component analysis and functional data clustering analysis. In this study, besides reviewing the developed unsupervised learning techniques, we extend unsupervised random forest clustering method to functional data and detect its shortages and strength through comparisons with other clustering methods in simulation studies. Finally, both proposed method and developed unsupervised learning techniques are conducted on a real data application: the analysis of the accuracy of the U.S. temperature prediction from 2014 to 2017.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Jiguo Cao
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: