14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeB2.3

Boets, Jeroen (Katholieke Universiteit Leuven), De Cock, Katrien (Katholieke Universiteit Leuven), De Moor, Bart (Katholieke Universiteit Leuven)

Distances between Dynamical Models for Clustering Time Series

Scheduled for presentation during the Regular Session "Identification of Linear Systems I" (WeB2), Wednesday, March 29, 2006, 16:10−16:30, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Time Series, Machine Learning and Data Mining


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.