RT Journal Article
T1 On-line Elastic Similarity Measures for time series
A1 Oregi, Izaskun
A1 Pérez, Aritz
A1 Del Ser, Javier
A1 Lozano, Jose A.
AB The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are similar to each other. Indeed, in off-line time series mining, these measures have been shown to be very effective due to their ability to handle time distortions and mitigate their effect on the resulting distance. In the on-line setting, where available data increase continuously over time and not necessary in a stationary manner, stream mining approaches are required to be fast with limited memory consumption and capable of adapting to different stationary intervals. In this sense, the computational complexity of Elastic Similarity Measures and their lack of flexibility to accommodate different stationary intervals, make these similarity measures incompatible with the requirements mentioned. To overcome these issues, this paper adapts the family of Elastic Similarity Measures – which includes Dynamic Time Warping, Edit Distance, Edit Distance for Real Sequences and Edit Distance with Real Penalty – to the on-line setting. The proposed adaptation is based on two main ideas: a forgetting mechanism and the incremental computation. The former makes the similarity consistent with streaming time series characteristics by giving more importance to recent observations, whereas the latter reduces the computational complexity by avoiding unnecessary computations. In order to assess the behavior of the proposed similarity measure in on-line settings, two different experiments have been carried out. The first aims at showing the efficiency of the proposed adaptation, to do so we calculate and compare the computation time for the elastic measures and their on-line adaptation. By analyzing the results drawn from a distance-based streaming machine learning model, the second experiment intends to show the effect of the forgetting mechanism on the resulting similarity value. The experimentation shows, for the aforementioned Elastic Similarity Measures, that the proposed adaptation meets the memory, computational complexity and flexibility constraints imposed by streaming data.
SN 0031-3203
YR 2019
FD 2019-04
LK https://hdl.handle.net/11556/3281
UL https://hdl.handle.net/11556/3281
LA eng
NO Oregi , I , Pérez , A , Del Ser , J & Lozano , J A 2019 , ' On-line Elastic Similarity Measures for time series ' , Pattern Recognition , vol. 88 , pp. 506-517 . https://doi.org/10.1016/j.patcog.2018.12.007
NO Publisher Copyright: © 2018 Elsevier Ltd
NO This work has been supported by the Basque Government through the EMAITEK program as well as through the ELKARTEK program (TEKINTZE project, ref. KK-2018/00104). Aritz Pérez and José A. Lozano are both supported by the Basque Government through the BERC 2018–2021 program and by the Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa excellence accreditation SEV-2017-0718. José A. Lozano is also supported by Spanish Ministry of Economy and Competitiveness MINECO through TIN2016-78365-R, and Aritz Pérez through TIN2017-82626-R funded by (AEI/FEDER, UE).
DS TECNALIA Publications
RD 31 jul 2024