NDLI: A combination of CUSUM-EWMA for Anomaly Detection in time series data

Content Provider	IEEE Xplore Digital Library
Author	Christodoulou, V. Yaxin Bi
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. & Math., Ulster Univ., Newtownabbey, UK (Christodoulou, V.; Yaxin Bi)
Abstract	In this work we investigate the use of parametric statistical methods for Anomaly Detection in time series data. The approach involves the use of simple and computationally efficient algorithms, the Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA), that have demonstrated an acceptable performance in detecting different shifts from the process mean. However, while the performance of these algorithms is found to be adequate in datasets where anomalies have a profound form, they produce many false positives when anomalies become more complex. To address this limitation, we propose a solution that has greater flexibility, in the form of a combined CUSUM-EWMA algorithm. Four different statistical methods are investigated and implemented, including the classic CUSUM and EWMA, and two variants of a combined CUSUM-EWMA algorithm. These algorithms have been evaluated on ten benchmark datasets. The F-Score for each of the algorithms has been used to measure their performance appropriately. The preliminary experimental results prove to be promising for the proposed method in detecting anomalies from time series data.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	8
File Size	1155008
Page Count	8
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344899
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Algorithm design and analysis Statistical analysis Time series analysis Process control Data models Robustness
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Granger Causality for Time-Series Anomaly Detection

Combining wavelet analysis and CUSUM algorithm for network anomaly detection

Probabilistic reasoning for streaming anomaly detection

Anomaly detection in temperature data using DBSCAN algorithm

MIMO EWMA-CUSUM condition-based Statistical Process Control in Manufacturing Processes

A statistical fault detection strategy using PCA based EWMA control schemes

The Usage of Time Series Control Charts for Financial Process Analysis

Usage of control charts for time series analysis in financial management

Research on Multi-class CUSUM Algorithm for Anomaly Detection of WSN

A combination of CUSUM-EWMA for Anomaly Detection in time series data

Similar Documents

Granger Causality for Time-Series Anomaly Detection

Combining wavelet analysis and CUSUM algorithm for network anomaly detection

Probabilistic reasoning for streaming anomaly detection

Anomaly detection in temperature data using DBSCAN algorithm

MIMO EWMA-CUSUM condition-based Statistical Process Control in Manufacturing Processes

A statistical fault detection strategy using PCA based EWMA control schemes

The Usage of Time Series Control Charts for Financial Process Analysis

Usage of control charts for time series analysis in financial management

Research on Multi-class CUSUM Algorithm for Anomaly Detection of WSN

A combination of CUSUM-EWMA for Anomaly Detection in time series data