A Simple Method for Unsupervised Anomaly Detection: An Application to Web Time Series Data

29 Pages Posted: 2 Jul 2021 Last revised: 4 Oct 2021

See all articles by Keisuke Yoshihara

Keisuke Yoshihara

Gunma University - Center for Mathematics and Data Science

Kei Takahashi

Fukuoka Institute of Technology - Faculty of Information Engineering

Date Written: October 4, 2021

Abstract

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 likelihood ratio estimated by the dynamic linear model, i.e. the ratio of 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 Benchmark (NAB) data set, publicly available and commonly used benchmark data sets, we find that our method achieves better or comparable performance compared to the existing methods. The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. In addition, we apply the proposed method to unlabeled Web time series data, specifically, daily page view and average session duration data on an electronic commerce site that deals in insurance goods to show the applicability of our method to unlabeled real-world data. We find that the increase in page view caused by e-mail newsletter deliveries is less likely to contribute to completing an insurance contract. The result also suggests the importance of the simultaneous monitoring of more than one time series.

Keywords: Unsupervised Anomaly Detection, Dynamic Linear Model, Density Ratio Estimation, Web Time Series Data

Suggested Citation

Yoshihara, Keisuke and Takahashi, Kei, A Simple Method for Unsupervised Anomaly Detection: An Application to Web Time Series Data (October 4, 2021). Available at SSRN: https://ssrn.com/abstract=3871018 or http://dx.doi.org/10.2139/ssrn.3871018

Keisuke Yoshihara (Contact Author)

Gunma University - Center for Mathematics and Data Science

4-2 Aramakimachi
Maebashi, Gunma 371-8510
Japan

Kei Takahashi

Fukuoka Institute of Technology - Faculty of Information Engineering ( email )

Japan

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
56
Abstract Views
497
Rank
705,143
PlumX Metrics