Automatic Registration and Clustering of Time Series

TL;DR

This paper introduces TROUT, a novel method for automatic time series registration within clustering, using an efficient dissimilarity measure that improves alignment accuracy without requiring templates.

Contribution

The paper presents TROUT, a new dissimilarity measure and algorithm for automatic time series alignment and clustering that outperforms existing methods.

Findings

TROUT achieves superior clustering accuracy compared to competitors.

The method effectively aligns time series without pre-defined templates.

TROUT is computationally efficient and statistically robust.

Abstract

Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals. Typical approaches include pre-registration to a user-specified template or time warping approaches which attempt to optimally align series with a minimum of distortion. For many signals obtained from recording or sensing devices, these methods may be unsuitable as a template signal is not available for pre-registration, while the distortion of warping approaches may obscure meaningful temporal information. We propose a new method for automatic time series alignment within a clustering problem. Our approach, Temporal Registration using Optimal Unitary Transformations (TROUT), is based on a novel dissimilarity measure between time series that is easy to compute and automatically identifies optimal alignment between pairs of time series. By embedding our new measure in a optimization formulation, we retain well-known advantages of computational and statistical performance. We provide an efficient algorithm for TROUT-based clustering and demonstrate its superior performance over a range of competitors.