Abstract
Several non-parametric statistics originally designed to test the equality of the log-spectra of two stochastic processes are proposed as dissimilarity measures between two time series. 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 are considered: (1) to distinguish between stationary and non-stationary time series, (2) to classify different ARMA processes and (3) 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 non-parametric distances showed the most robust behaviour.
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Díaz, S.P., Vilar, J.A. (2010). A Comparative Study of Several Parametric and Semiparametric Approaches for Time Series Classification. In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_15
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DOI: https://doi.org/10.1007/978-3-642-10745-0_15
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