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Dynamic graph embedding for outlier detection on multiple meteorological time series

Gen Li, Jason J Jung
2021 PLoS ONE  
To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE.  ...  Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related  ...  The study by [10] identified four types of outliers in a dynamic graph, which are abnormal vertices, abnormal edges, abnormal subgraphs, and event detection.  ... 
doi:10.1371/journal.pone.0247119 pmid:33600442 pmcid:PMC7891775 fatcat:ecfsirbke5aovnlkervym2vyeu

Traffic dispersion graph based anomaly detection

Do Quoc Le, Taeyoel Jeong, H. Eduardo Roman, James Won-Ki Hong
2011 Proceedings of the Second Symposium on Information and Communication Technology - SoICT '11  
We analyze differences of TDG graphs in time series to detect anomalies and introduce a method to identify attack patterns in anomalous traffic.  ...  In this paper, we propose a novel approach to detect anomalous network traffic based on graph theory concepts such as degree distribution, maximum degree and dK-2 distance.  ...  [10] represented network traffic by means of a series of related graphs that change over time, using several graph metrics.  ... 
doi:10.1145/2069216.2069227 dblp:conf/soict/LeJRH11 fatcat:yg2qmne4mbg7nbbll4wxdei5oi

Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

Ya-nan Wang, Jian Wang, Xiaoshi Fan, Yafei Song
2020 IEEE Access  
by similarity, and establish an intuitionistic fuzzy time series graph of the traffic data in the time dimension.  ...  time series graph mining.  ...  A complete five-vertex graph G i (V i , E i )(i = 1, 2, . . . , t) at each time in the historical data is constructed to obtain an IFTS graph over the entire time series, and a time-based forecasting graphĜ  ... 
doi:10.1109/access.2020.2983986 fatcat:wzivibootvfh5bahjdgyxa7poe

Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series

Gen Li, Jason J. Jung
2021 Scientific Reports  
To overcome this problem, we construct a dynamic graph by discovering the correlation among the climate time series and propose a novel dynamic graph embedding model based on graph entropy called EDynGE  ...  AbstractAbnormal climate event is that some meteorological conditions are extreme in a certain time interval.  ...  We propose a novel idea to detect outliers from multiple time series. It utilizes the correlation of the time series to construct a dynamic graph and detects the outlier from the dynamic graph.  ... 
doi:10.1038/s41598-021-92973-8 fatcat:6i5ls42t5rftzjcy4sbta5h2pq

Anomaly, event, and fraud detection in large network datasets

Leman Akoglu, Christos Faloutsos
2013 Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13  
As objects in graphs have long-range correlations, novel technology has been developed for abnormality detection in graph data.  ...  The goal of this tutorial is to provide a general, comprehensive overview of the state-of-the-art methods for anomaly, event, and fraud detection in data represented as graphs.  ...  Anomalies in node-/edge-labeled graphs [4, 8] Part II: Event detection in dynamic data (a) Overview: Event detection in time series of data (b) Event detection in time series of graph data i.  ... 
doi:10.1145/2433396.2433496 dblp:conf/wsdm/AkogluF13 fatcat:i7m6c3g7j5agvnova3javx3aly

The effect of methods of eliminating spikes in the time series of freight flows on their statistical characteristics

Sergey Gritsay, Alexandr Lashchenykh, Serhii Turpak, Elena Ostroglyad, Tamara Kharchenko
2017 Eastern-European Journal of Enterprise Technologies  
Detection of abnormal values. The graph in Fig. 1 shows a close grouping of several abnormal values in the considered time series.  ...  Characteristics of the FDR initial time series of supply volume: a -the graph of the initial time series; b -the graph of the sample autocorrelation function In Fig. 1 , a with the initial time series  ...  S h a p a t i n a Assistant** E-mail: shapatina@ukr.net *Department of automation and computer remote control train traffic*** **Department of manage freight and commercial work*** ***Ukrainian State University  ... 
doi:10.15587/1729-4061.2017.92528 fatcat:y7dfk7tkqzdzvbcbr2aczxr2e4

Investigation of graph edit distance cost functions for detection of network anomalies

Kelly Marie Kapsabelis, Peter John Dickinson, Kutluyil Dogancay
2007 ANZIAM Journal  
A recent novel approach represents the logical communications of a periodically observed network as a time series of graphs and applies the graph matching technique, graph edit distance, to monitor and  ...  There has been significant research in the detection of change and anomalous events in computer networks.  ...  When repeated across the complete time series of graphs this method produces a time series of numbers that represents the amount of change the network exhibits over time.  ... 
doi:10.21914/anziamj.v48i0.47 fatcat:i4iwqlzknra5nohunjp6gxf2ze

Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems [chapter]

Alireza Vahdatpour, Majid Sarrafzadeh
2010 Proceedings of the 2010 SIAM International Conference on Data Mining  
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data.  ...  A graph clustering approach is used to construct the frequent activity structures. Such structures are used to locate normal and abnormal occurrences of activities in time series.  ...  The authors wish to thank Professor Eamonn Keogh for providing us with the implementation of the motif detection algorithm.  ... 
doi:10.1137/1.9781611972801.56 dblp:conf/sdm/VahdatpourS10 fatcat:vt6ml5e6mndjjkxe5fpbcs7xam

Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics [chapter]

Valeriy Gavrishchaka, Olga Senyukova, Kristina Davis
2014 Advances in Experimental Medicine and Biology  
neurological abnormalities using gait time series.  ...  time series.  ...  A spanning tree is a connected graph containing all vertices of the original graph without loops, i.e., there exists only one path connecting any two pairs of nodes in the graph.  ... 
doi:10.1007/978-3-319-10984-8_6 pmid:25381104 fatcat:hyhr75hynvfbvixapxnv5ddtma

Anomaly detection in dynamic networks: a survey

Stephen Ranshous, Shitian Shen, Danai Koutra, Steve Harenberg, Christos Faloutsos, Nagiza F. Samatova
2015 Wiley Interdisciplinary Reviews: Computational Statistics  
As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.  ...  Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data.  ...  Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Laboratory for Analytic Sciences and/or any  ... 
doi:10.1002/wics.1347 fatcat:44znvnsmlfcgfbbljcvmuubgpi

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

Marjan Momtazpour, Jinghe Zhang, Saifur Rahman, Ratnesh Sharma, Naren Ramakrishnan
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
In particular we illustrate how this approach helps rapidly identify outliers during system operation.  ...  We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system.  ...  invariant graph of the system. • Detecting system outliers based on the change in the graph of invariants and ranking time series for fault localization.  ... 
doi:10.1145/2783258.2788605 dblp:conf/kdd/MomtazpourZRSR15 fatcat:4dhd4n45cvdhxi3szxqif2s4ey

The human behaviour indicator: A measure of behavioural evolution

Abubaker Elbayoudi, Ahmad Lotfi, Caroline Langensiepen
2019 Expert systems with applications  
represented in various time series can be visualised in a simple and more understandable format; (3) identifying trends in ADLs or ADWs is a relevant means of sharing information with carers or supervisors  ...  Activities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a person's health or well-being.  ...  The HBI technique is proposed and applied to binary time series to measure progressive changes in human behaviour and to detect abnormal behaviour.  ... 
doi:10.1016/j.eswa.2018.10.022 fatcat:ccnov3bjw5dlzfca7nxw4di3jy

Traffic change point detection and analysis

2021 jecet  
Its basic definition is that in a sequence or process, when a certain statistical characteristic (distribution type, distribution parameter) changes at a certain point in time by systemic factors rather  ...  Finally, the dynamic algorithm is used to detect and estimate the actual number and position of the actual speed data of Beijing Road in Zhangdian District of Zibo City, and a result of troubleshooting  ...  Anomalous: Time Series Given a set of time series, the abnormal time series is the part of X that is inconsistent with most time series values.  ... 
doi:10.24214/jecet.c.10.1.03644 fatcat:drhz2cg6qbarvkngkpsxle2xzy

Anomaly localization for network data streams with graph joint sparse PCA

Ruoyi Jiang, Hongliang Fei, Jun Huan
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Our key observation is that we can localize anomalies by identifying a sparse low dimensional space that captures the abnormal events in data streams.  ...  Principal Component Analysis (PCA) has been extensively applied to detecting anomalies in network data streams.  ...  Acknowledgments The work is partially supported by the NSF award IIS 0845951 and the Office of Naval Research N00014-07-1-1042.  ... 
doi:10.1145/2020408.2020557 dblp:conf/kdd/JiangFH11 fatcat:jpwsntyfpffwrfwl4vb56ueixu

Change-point detection for monitoring clinical decision support systems with a multi-process dynamic linear model

Siqi Liu, Adam Wright, Dean F. Sittig, Milos Hauskrecht
2017 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
To detect changes of the monitoring and alerting component, it would be ideal to have measurements on different components of the CDSS. However, in reality, it is not feasible to  ...  We develop a new change-point detection method using the Multi-Process Dynamic Linear Model.  ...  The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.  ... 
doi:10.1109/bibm.2017.8217712 pmid:29302379 pmcid:PMC5749419 dblp:conf/bibm/LiuWSH17 fatcat:ve2zru6wwng5vn3dxijwduqvay
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