Top

Artificial Intelligence Review

Published in:

03-01-2019

Detecting anomalies in sequential data augmented with new features

Authors: Xiangzeng Kong, Yaxin Bi, David H. Glass

Published in: Artificial Intelligence Review | Issue 1/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper presents a new weighted local outlier factor method for anomaly detection, which is underpinned with three novel components: (1) a piecewise linear representation defined on the basis of the important points that consist of extreme points and additional points; (2) a set of new features which are used to identify anomalies given the new piecewise linear representation; (3) a weighting schema, assigning different weights to different features by accounting for the discriminant power of the features. The underlying idea of the proposed method is to characterize a time series with a set of four features and then discover abnormal changes by taking account of the closeness of any data points augmented with the new features. The comparative experiments demonstrate that the proposed piecewise representation method has performed well in sequential time series data, and the weighted local outlier factor method has achieved better accuracy and RankPower in detecting anomalies from the same data sets in comparison with the conventional local outlier factor, normalized local outlier factor and HOT symbolic aggregate approximation methods.

previous article A novel exponential distance and its based TOPSIS method for interval-valued intuitionistic fuzzy sets using connection number of SPA theory

next article Robust visual line-following navigation system for humanoid robots

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Aydin I, Karakose M, Akin E (2015) Anomaly detection using a modified kernel-based tracking in the pantograph-catenary system. Expert Syst Appl 42(2015):938–948CrossRef

Beigi MS, Chang SF, Ebadollahi S, Verma DC (2011) Anomaly detection in information streams without prior domain knowledge. IBM J Res Dev 55(5):1–11CrossRef

Breunig MM, Kriegel H-P, Ng RN, Sander J (2000) LOF: identifying density-based local outliers. In: Proceeding SIGMOD’00 proceedings of the 2000 ACM SIGMOD international conference on management of data, vol 29(2). ACM, New York, pp 93–104

Chandola V, Boriah S, Kumar V (2008a) Understanding categorical similarity measures for outlier detection. Technical report 08-008, University of Minnesota, pp 1–45

Chandola V, Mithal V, Kumar V (2008b) A comparative evaluation of anomaly detection techniques for sequence data. In: ICDM, pp 743–748

Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58CrossRef

Gupta M, Gao J, Aggarwal CC, Han J (2014) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 26(9):2250–2267CrossRef

Hadi AS (1994) A modification of a method for the detection of outliers in multivariate samples. J R Stat Soc B 56(2):393–396MATH

Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22(2):85–126CrossRef

Huang H (2013) Rank based anomaly detection algorithms. Dissertations, Electrical Engineering and Computer Science, pp 1–182

Jin XH, Sun Y, Que ZJ, Wang Y, Chow WS (2016) Anomaly detection and fault prognosis for bearings. IEEE Trans Instrum Meas 65(9):2046–2054CrossRef

Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 151–162

Keogh E, Lonardi S, Ratanamahatana CA (2004) Towards parameter-free data mining. KDD, Seattle, Washington, DC, pp 206–215

Keogh E, Lin J, Fu A (2005) Hot sax: efficiently finding the most unusual time series subsequence. In: ICDM, pp 226–233

Keogh E, Lin J, Lee SH, Herle HV (2006) Finding the most unusual time series subsequence: algorithms and applications. Knowl Inf Syst 11(1):1–27. http://www.cs.ucr.edu/~eamonn/ CrossRef

Keogh E, Chakrabarti K, Pazzani MJ, Mehrotra S (2008) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263–268CrossRef

Kou Y, Lu CT, Chen D (2006) Spatial weighted outlier detection. In: Proceedings of the SIAM conference on data mining, pp 614–617

Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery, pp 2–11

Palpanas T, Vlachos M, Keogh E, Gunopulos D, Truppel W (2004) Online amnesic approximation of streaming time series. In: ICDE, Boston, March 2004

Park S, Kim SW, Cho JS, Padmanabhan S (2001a) Prefix-querying: an approach for effective subsequence matching under time warping in sequence databases. In: Proceedings of the 10th international conference on information and knowledge management, pp 255–262

Park S, Kim SW, Chu WW (2001b) Segment-based approach for subsequence searches in sequence databases. In: Proceedings of the 16th ACM symposium on applied computing, pp 248–252

Peng CS, Wang H, Zhang SR, Parker DS (2000) Landmarks: a new model for similarity-based pattern querying in time series databases. In: Proceedings of the 16th international conference on data engineering, pp 33–42

Pratt KB, Fink E (2002) Search for patterns in compressed time series. Int J Image Graph 2(1):89–106CrossRef

Ramaswamy S, Rastogi R, Kyuseok S (2000) Efficient algorithms for mining outliers from large data sets. In: Proceeding ACMSIGMOD international conference on management of data, pp 427–438

Sun J, Qu H, Chakrabarti D, Faloutsos C (2005) Neighborhood formation and anomaly detection in bipartite graphs. In: Proceedings of the 5th IEEE international conference on data mining. IEEE Computer Society, pp 418–425

Tandon G, Chan P (2007) Weighting versus pruning in rule validation for detecting network and host anomalies. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 697–706

Weigend AS, Mangeas M, Srivastava AN (1995) Nonlinear gated experts for time-series: discovering regimes and avoiding overfitting. Int J Neural Syst 6(4):373–399CrossRef

Yan C, Fang J, Wu L, Ma S (2013) An approach of time series piecewise linear representation based on local maximum minimum and extremum. J Inf Comput Sci 10(9):2747–2756CrossRef

Yankov D, Keogh E, Rebbapragada U (2007) Disk aware discord discovery: finding unusual time series in terabyte sized datasets. In: ICDM 2007

Zhang Y, Meratnia N, Havinga PJM (2008) Outlier detection techniques for wireless sensor networks: a survey. Technical Report, Centre Telemat. Inform. Technol. Univ. Twente, Enschede, TR-CTIT-08-59, pp 159–170

Title: Detecting anomalies in sequential data augmented with new features
Authors: Xiangzeng Kong
Yaxin Bi
David H. Glass
Publication date: 03-01-2019
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 1/2020
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-018-9671-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 1/2020

Shilling attacks against collaborative recommender systems: a review

A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations

Reflective agents for personalisation in collaborative games

Knowledge discovery and visualization in antimicrobial resistance surveillance systems: a scoping review

Robust visual line-following navigation system for humanoid robots

Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review

Premium Partner