Artículo
A multi-scale smoothing kernel for measuring time-series similarity
Autor/es | Troncoso Lora, Alicia
Arias, Marta Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2015 |
Fecha de depósito | 2016-07-14 |
Publicado en |
|
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 ... 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 distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered. |
Identificador del proyecto | TIN2011-27479-C04-03
TIN2011-28956-C02 2009-SGR-1428 P12-TIC-1728 APPB813097 FP7-ICT-216886 BE-DGR2011 |
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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
A mutil scale.pdf | 1.569Mb | [PDF] | Ver/ | |