Katrien De Cock

Subspace angles between ARMA models

Abstract We define a notion of subspace angles between two linear, autoregressive moving average, single-input–single-output models by considering the principal angles between subspaces that are derived from these models. We show how a recently defined metric for these models, which is based on their cepstra, relates to the subspace angles between the models.

On a cepstral norm for an ARMA model and the polar plot of the logarithm of its transfer function

In this paper, we show that a recently defined cepstral norm for ARMA models equals, up to a constant factor, the square root of the area enclosed by the polar plot of the logarithm of the transfer function.

Distances between dynamical models for clustering time series

Abstract In this paper we consider the clustering of time series arising from the class of scalar linear stochastic models. The properties and performance of several so-called model-free and model-based distances for these time series are compared on both artificial and real data sets. In particular, the inappropriateness of model-free distances to distinguish between time series of this class is shown, as well as several important differences between the model-based distances themselves.