Journal of Classification

, Volume 27, Issue 3, pp 333–362

Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study


DOI: 10.1007/s00357-010-9064-6

Cite this article as:
Díaz, S.P. & Vilar, J.A. J Classif (2010) 27: 333. doi:10.1007/s00357-010-9064-6


One key point in cluster analysis is to determine a similarity or dissimilarity measure between data objects. When working with time series, the concept of similarity can be established in different ways. In this paper, several non-parametric statistics originally designed to test the equality of the log-spectra of two stochastic processes are proposed as dissimilarity measures between time series data. Their behavior in time series clustering is analyzed throughout a simulation study, and compared with the performance of several model-free and model-based dissimilarity measures. Up to three different classification settings were considered: (i) to distinguish between stationary and non-stationary time series, (ii) to classify different ARMA processes and (iii) to classify several non-linear time series models. As it was expected, the performance of a particular dissimilarity metric strongly depended on the type of processes subjected to clustering. Among all the measures studied, the nonparametric distances showed the most robust behavior.


Time series clustering Dissimilarity measures Stationary and non-stationary processes ARMA processes Non-linear processes Local linear regression 

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Unidad de Epidemiología Clínica y, Bioestadística, Complejo HospitalarioUniversitario de A CoruñaCoruñaSpain
  2. 2.Departamento de MatemáticasUniversidade de A CoruñaCoruñaSpain

Personalised recommendations