Evaluation metrics for anomaly detection algorithms in time-series

被引：6

作者：

Kovacs, Gyorgy ^{[1
]}

Sebestyen, Gheorghe ^{[1
]}

Hangan, Anca ^{[1
]}

机构：

[1] Tech Univ Cluj Napoca, Cluj Napoca, Romania

来源：

ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA | 2019年 / 11卷 / 02期

关键词：

anomaly detection; classification; evaluation metrics;

D O I：

10.2478/ausi-2019-0008

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.

引用

页码：113 / 130

页数：18

共 50 条

[1] An Evaluation of Time-Series Anomaly Detection in Computer Networks
Nguyen, Hong
Hajisafi, Arash
Abdoli, Alireza
Kim, Seon Ho
Shahabi, Cyrus
[J]. 2023 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN, 2023, : 104 - 109
[2] Towards a Rigorous Evaluation of Time-Series Anomaly Detection
Kim, Siwon
Choi, Kukjin
Choi, Hyun-Soo
Lee, Byunghan
Yoon, Sungroh
[J]. THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 7194 - 7201
[3] Local Evaluation of Time Series Anomaly Detection Algorithms
Huet, Alexis
Navarro, Jose Manuel
Rossi, Dario
[J]. PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 635 - 645
[4] Contrastive Time-Series Anomaly Detection
Kim, Hyungi
Kim, Siwon
Min, Seonwoo
Lee, Byunghan
[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (10) : 5053 - 5065
[5] Adaptive Multivariate Time-Series Anomaly Detection
Lv, Jianming
Wang, Yaquan
Chen, Shengjing
[J]. INFORMATION PROCESSING & MANAGEMENT, 2023, 60 (04)
[6] Granger Causality for Time-Series Anomaly Detection
Qiu, Huida
Liu, Yan
Subrahmanya, Niranjan A.
Li, Weichang
[J]. 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012), 2012, : 1074 - 1079
[7] Time-Series Anomaly Detection Service at Microsoft
Ren, Hansheng
Xu, Bixiong
Wang, Yujing
Yi, Chao
Huang, Congrui
Kou, Xiaoyu
Xing, Tony
Yang, Mao
Tong, Jie
Zhang, Qi
[J]. KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 3009 - 3017
[8] Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
Sondre Sørbø
Massimiliano Ruocco
[J]. Data Mining and Knowledge Discovery, 2024, 38 : 1027 - 1068
[9] Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
Sorbo, Sondre
Ruocco, Massimiliano
[J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2024, 38 (03) : 1027 - 1068
[10] Suboptimal Partitioning of Time-series Data for Anomaly Detection
Jin, Xin
Sarkar, Soumik
Mukherjee, Kushal
Ray, Asok
[J]. PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009), 2009, : 1020 - 1025

← 1 2 3 4 5 →