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Pierpaolo D'Urso, Livia De Giovanni, Elizabeth Ann Maharaj and Riccardo Massari Wavelet-based self-organizing maps for classifying multivariate time series Journal of Chemometrics 28

Version of Record online: 11 NOV 2013 | DOI: 10.1002/cem.2565

We suggest a time series clustering method that combines the benefits connected to the interpretative power of wavelet representations of the time series and the informational gain of clustering and vector quantization connected to the adopted self-organizing map technique. Our clustering method considers composite wavelet-based information that combines and tunes information connected to the wavelet variance and wavelet correlation. To assess the effectiveness of the proposed clustering approach, results of simulation studies and an empirical application are given.

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