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A simple method for unsupervised anomaly detection: An application to Web time series data
2022
PLoS ONE
We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based on the state space model. Our detection rule is based on the ratio of log-likelihoods estimated by the dynamic linear model, i.e. the ratio of log-likelihood in our model to that in an over-dispersed model that we will call the NULL model. Using the Yahoo S5 data set and the Numenta Anomaly
doi:10.1371/journal.pone.0262463
pmid:35015791
pmcid:PMC8752013
fatcat:bekblaruy5bmdo6x2vasnipesm