Carnegie Mellon University
Browse
file.pdf (4.48 MB)

A Conformal Prediction Approach to Explore Functional Data

Download (4.48 MB)
journal contribution
posted on 2005-10-01, 00:00 authored by Jing LeiJing Lei, Alessandro Rinaldo, Larry Wasserman

This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions. The prediction sets are also informative in that they correspond to the high density region of the underlying process. While ordinary conformal prediction has high computational cost for functional data, we use the inductive conformal predictor, together with several novel choices of conformity scores, to simplify the computation. Our methods are illustrated on some real data examples.

History

Publisher Statement

All Rights Reserved

Date

2005-10-01

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC