Published July 14, 2016
| Version v1
Publication
A multi-scale smoothing kernel for measuring time-series similarity
Description
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.
Abstract
Ministerio de Ciencia y Tecnología TIN2011-27479-C04-03Abstract
Ministerio de Ciencia y Tecnología TIN2011-28956-C02Abstract
Generalitat de Catalunya 2009-SGR-1428Abstract
Junta de Andalucía P12-TIC-1728Abstract
Universidad Pablo de Olavide APPB813097Abstract
Unión Europea Pascal2 Network of Excellence FP7-ICT-216886Abstract
Generalita de Catalunya BE-DGR2011Additional details
Identifiers
- URL
- https://idus.us.es/handle/11441/43595
- URN
- urn:oai:idus.us.es:11441/43595