Feature Sensitive and Automated Curve Registration

TL;DR

This paper introduces a new method for aligning functional data with different time distortions, demonstrating its consistency, superior performance in simulations, and application to paleoclimatic data analysis.

Contribution

A novel, consistent curve registration method that outperforms existing techniques and is applicable to real-world paleoclimatic data.

Findings

Proposed method is consistent under general conditions.

Simulation results show superior performance over existing methods.

Successfully applied to paleoclimatic data sets.

Abstract

Given two sets of functional data having a common underlying mean function but different degrees of distortion in time measurements, we provide a method of estimating the time transformation necessary to align (or `register') them. We prove that the proposed method is consistent under fairly general conditions. Simulation results show superiority of the performance of the proposed method over two existing methods. The proposed method is illustrated through the analysis of three paleoclimatic data sets.