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Assessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcare

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorBonilla Escribano, Pablo
dc.contributor.authorRamírez García, David
dc.contributor.authorPorras Segovia, Alejandro
dc.contributor.authorArtés Rodríguez, Antonio
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-06-11T08:45:08Z
dc.date.available2021-06-11T08:45:08Z
dc.date.issued2021-01-01
dc.description.abstractVariability is defined as the propensity at which a given signal is likely to change. There are many choices for measuring variability, and it is not generally known which ones offer better properties. This paper compares different variability metrics applied to irregularly (nonuniformly) sampled time series, which have important clinical applications, particularly in mental healthcare. Using both synthetic and real patient data, we identify the most robust and interpretable variability measures out of a set 21 candidates. Some of these candidates are also proposed in this work based on the absolute slopes of the time series. An additional synthetic data experiment shows that when the complete time series is unknown, as it happens with real data, a non-negligible bias that favors normalized and/or metrics based on the raw observations of the series appears. Therefore, only the results of the synthetic experiments, which have access to the full series, should be used to draw conclusions. Accordingly, the median absolute deviation of the absolute value of the successive slopes of the data is the best way of measuring variability for this kind of time series.en
dc.format.extent18
dc.identifier.bibliographicCitationBonilla-Escribano, P., Ramírez, D., Porras-Segovia, A., & Artés-Rodríguez, A. (2021). Assessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcare. Mathematics, 9(1), 71.en
dc.identifier.doihttps://doi.org/10.3390/math9010071
dc.identifier.issn2227-7390
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue71
dc.identifier.publicationlastpage18
dc.identifier.publicationtitleMathematicsen
dc.identifier.publicationvolume9 (1)
dc.identifier.urihttps://hdl.handle.net/10016/32864
dc.identifier.uxxiAR/0000027981
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDGobierno de España. TEC2017-86921-C2-2-Res
dc.relation.projectIDGobierno de España. TEC2017-92552-EXPes
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705es
dc.relation.projectIDGobierno de España. RTI2018-099655-B-I00es
dc.rights© 2020 by the authors. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherEcological momentary assessment (EMA)en
dc.subject.otherHawkes processen
dc.subject.otherIrregularly sampled time seriesen
dc.subject.otherVariabilityen
dc.titleAssessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcareen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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