Applicability and interpretation of the deterministic weighted cepstral distance

TL;DR

This paper extends the weighted cepstral distance to linear time-invariant models, providing a data-driven method to analyze system stability and phase characteristics for applications like time series clustering.

Contribution

It introduces an extension of the weighted cepstral distance for LTI models and links it to system stability and phase, enabling analysis without system identification.

Findings

The extended distance can be interpreted via poles and zeros.

A method for assessing stability and phase type from input/output data.

Connection established between the distance and a model norm.

Abstract

Quantifying similarity between data objects is an important part of modern data science. Deciding what similarity measure to use is very application dependent. In this paper, we combine insights from systems theory and machine learning, and investigate the weighted cepstral distance, which was previously defined for signals coming from ARMA models. We provide an extension of this distance to invertible deterministic linear time invariant single input single output models, and assess its applicability. We show that it can always be interpreted in terms of the poles and zeros of the underlying model, and that, in the case of stable, minimum-phase, or unstable, maximum-phase models, a geometrical interpretation in terms of subspace angles can be given. We then devise a method to assess stability and phase-type of the generating models, using only input/output signal information. In this way, we prove a connection between the extended weighted cepstral distance and a weighted cepstral model norm. In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step. This can be useful in machine learning tasks such as time series clustering. An iPython tutorial is published complementary to this paper, containing implementations of the various methods and algorithms presented here, as well as some numerical illustrations of the equivalences proven here.