Pradhan, Ipsit (2016) Anomaly Detection on Time Series Data. MTech thesis.
Anomaly detection is an important problem that has been researched within diverse application domains. Detection of anomalies in the time series domain finds extensive application in monitoring system status, mal-ware/spam detection, credit-card fraud etc. In this work we explore methods to detect anomalies in multivariate as well as uni variate time-series and proposed a novel method using Dictionary Learning, Sparse Representation, Singular Value Decomposition and Topological anomaly detection(TAD). We have tested the proposed method on real as well as synthetic data sets. Our novel method brings down the false positive rates as compared to the existing methods.
|Item Type:||Thesis (MTech)|
|Uncontrolled Keywords:||Anomaly Detection, Time-series, Dictionary Learning, Electricity Theft Detection, Unsupervised Techniques|
|Subjects:||Engineering and Technology > Computer and Information Science > Data Mining|
Engineering and Technology > Computer and Information Science > Information Security
|Divisions:||Engineering and Technology > Department of Computer Science Engineering|
|Deposited By:||Mr. Sanat Kumar Behera|
|Deposited On:||18 Sep 2017 17:05|
|Last Modified:||05 Dec 2019 18:04|
|Supervisor(s):||Patra, Bidyut Kumar|
Repository Staff Only: item control page