Functional neural networks for scalar prediction

Author: 
Date created: 
2020-04-07
Identifier: 
etd20795
Keywords: 
Functional Data Analysis
Machine Learning
Neural Networks
Prediction
Abstract: 

We introduce a methodology for integrating functional data into densely connected feed-forward neural networks. The model is defined for scalar responses with at least one functional covariate and some number of scalar covariates. A by-product of the method is a set of functional parameters that are dynamic to the learning process which leads to interpretability. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying coefficient function; these results were confirmed through cross-validations and simulation studies. A collection of useful functions are built on top of the Keras/Tensorflow architecture allowing for general use of the approach.

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): 
Senior supervisor: 
Jiguo Cao
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: