{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T14:28:43Z","timestamp":1725287323776},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a \u201cpoint approach\u201d), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a \u201cFourier approach\u201d). We refer to these approaches as Functional Kernel Density Estimation for Anomaly Detection as they both yield functionals that can score a time series for how anomalous it is. Both methods naturally handle missing data and apply to a variety of settings, performing well when compared with an outlyingness score derived from a boxplot method for functional data, with a Principal Component Analysis approach for functional data, and with the Functional Isolation Forest method. We illustrate the use of the proposed methods with aviation safety report data from the International Air Transport Association (IATA).<\/jats:p>","DOI":"10.3390\/e22121363","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T01:10:22Z","timestamp":1606785022000},"page":"1363","source":"Crossref","is-referenced-by-count":6,"title":["Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection"],"prefix":"10.3390","volume":"22","author":[{"given":"Michael R.","family":"Lindstrom","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of California, Los Angeles, CA 90024, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5456-0244","authenticated-orcid":false,"given":"Hyuntae","family":"Jung","sequence":"additional","affiliation":[{"name":"Global Aviation Data Management, International Air Transport Association (IATA), Montr\u00e9al, QC H2Y 1C6, Canada"}]},{"given":"Denis","family":"Larocque","sequence":"additional","affiliation":[{"name":"Department of Decision Sciences, HEC Montr\u00e9al, Montr\u00e9al, QC H2Y 1C6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cleophas, T.J., Zwinderman, A.H., and Cleophas-Allers, H.I. 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