# Anomaly Detection on IT Operation Series via Online Matrix Profile

arXiv: https://arxiv.org/abs/2108.12093 · HTML: https://mlsanity.com/paper/2108.12093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks](https://mlsanity.com/paper/2009.07769/) (2020)
- [VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection](https://mlsanity.com/paper/1907.01702/) (2019)

## Built on by (most cited first, 1 of 1)

- [Calculating the matrix profile from noisy data](https://mlsanity.com/paper/2306.10151/) (2023)

## Abstract (arXiv)

Anomaly detection on time series is a fundamental task in monitoring the Key Performance Indicators (KPIs) of IT systems. Many of the existing approaches in the literature show good performance while requiring a lot of training resources. In this paper, the online matrix profile, which requires no training, is proposed to address this issue. The anomalies are detected by referring to the past subsequence that is the closest to the current one. The distance significance is introduced based on the online matrix profile, which demonstrates a prominent pattern when an anomaly occurs. Another training-free approach spectral residual is integrated into our approach to further enhance the detection accuracy. Moreover, the proposed approach is sped up by at least four times for long time series by the introduced cache strategy. In comparison to the existing approaches, the online matrix profile makes a good trade-off between accuracy and efficiency. More importantly, it is generic to various types of time series in the sense that it works without the constraint from any trained model.