RT Journal Article
T1 Time series features and fuzzy memberships combination for time series classification
A1 Baldán, Francisco J.
A1 Martínez, Luis
K1 Análisis de series temporales
K1 Sistemas difusos
K1 Lógica difusa
AB Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results’ performance, leaving interpretability on a secondary level. The available interpretable proposals do not provide competitive results, which is an issue to be addressed. This paper introduces a new fuzzy feature-based time series classification method, which joins the ability of time series features to capture essential information about the time series with Fuzzy logic. This proposal allows the fuzzy-based approach to incorporate global information about the behavior of time series in the membership calculation with the aim of improving the performance and interpretability of the results by using an interpretable classifier. The proposed method has been evaluated over the 112 state-of-the-art time series classification datasets from the UCR repository, and the results obtained show a better performance. Furthermore, the combination of time series features and fuzzy memberships has also increased the interpretability of final models.
PB Elsevier
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32419
UL https://hdl.handle.net/10630/32419
LA eng
NO Francisco J. Baldán, Luis Martínez, Time series features and fuzzy memberships combination for time series classification, Neurocomputing, Volume 606, 2024, 128368, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2024.128368
NO Funding for open access charge: Universidad de Málaga / CBUA. F.J. Baldán was supported by grant FJC2021-047112-I funded by MICIU/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026