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A multi-scale smoothing kernel for measuring time-series similarity

Opened Access A multi-scale smoothing kernel for measuring time-series similarity

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Autor: Troncoso Lora, Alicia
Arias, Marta
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2015
Publicado en: Neurocomputing, 167, 8-17.
Tipo de documento: Artículo
Resumen: In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived dista...
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Cita: Troncoso Lora, A., Arias, M. y Riquelme Santos, J.C. (2015). A multi-scale smoothing kernel for measuring time-series similarity. Neurocomputing, 167, 8-17.
Tamaño: 1.569Mb
Formato: PDF

URI: http://hdl.handle.net/11441/43595

DOI: 10.1016/j.neucom.2014.08.099

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