000125911 001__ 125911
000125911 005__ 20240731103328.0
000125911 0247_ $$2doi$$a10.1016/j.softx.2023.101363
000125911 0248_ $$2sideral$$a133435
000125911 037__ $$aART-2023-133435
000125911 041__ $$aeng
000125911 100__ $$0(orcid)0000-0002-2605-6243$$aBernardi, S.$$uUniversidad de Zaragoza
000125911 245__ $$aTegdet: an extensible Python library for anomaly detection using time evolving graphs
000125911 260__ $$c2023
000125911 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125911 5203_ $$aThis paper presents tegdet, a new Python library for anomaly detection in unsupervised approaches. The input of the library is a univariate time series, representing observations of a given phenomenon. Then, tegdet identifies anomalous epochs, i.e., time intervals where the observations differ in a given percentile of a baseline distribution. Epochs are represented by time evolving graphs and the baseline distribution is given by the dissimilarities between a reference graph and the graphs of the epochs. Currently, the library implements 28 dissimilarity metrics, i.e., 28 different anomaly detection techniques, and its extensible design allows to easily introduce new ones. tegdet exposes a complete functionality to carry out the anomaly detection, through a straightforward designed API. Summarizing, to the best of our knowledge, tegdet is the first publicly available library, based on time evolving graphs, for anomaly detection in time series. Our experimentation shows promising results. For example, Clark and Divergence techniques can achieve an accuracy of 100%, while the time to build the model and predict lasts for few hundreds milliseconds.
000125911 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00
000125911 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000125911 590__ $$a2.4$$b2023
000125911 592__ $$a0.544$$b2023
000125911 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b54 / 131 = 0.412$$c2023$$dQ2$$eT2
000125911 593__ $$aComputer Science Applications$$c2023$$dQ2
000125911 593__ $$aSoftware$$c2023$$dQ3
000125911 594__ $$a5.5$$b2023
000125911 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125911 700__ $$aJavierre, R.
000125911 700__ $$0(orcid)0000-0002-8917-6584$$aMerseguer, J.$$uUniversidad de Zaragoza
000125911 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000125911 773__ $$g22 (2023), 101363 [7 pp.]$$tSoftwareX$$x2352-7110
000125911 8564_ $$s1437274$$uhttps://zaguan.unizar.es/record/125911/files/texto_completo.pdf$$yVersión publicada
000125911 8564_ $$s2425263$$uhttps://zaguan.unizar.es/record/125911/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125911 909CO $$ooai:zaguan.unizar.es:125911$$particulos$$pdriver
000125911 951__ $$a2024-07-31-09:45:32
000125911 980__ $$aARTICLE