Index terms temporal outlier detection, time series data, data streams, distributed data streams, temporal networks, spatio temporal outliers, applications of temporal outlier detection, network outliers 1introduction o utlier detection is a broad. Anomaly detection and characterization in spatial time series data. Abstract in the statistics community, outlier detection for time series data has been studied for decades. Spatiotemporal outlier detection in streaming trajectory data diva. Taking into account of spatial context in addition to temporal context would help uncovering complex anomaly types and unexpected and interesting knowledge about.
In this paper, we place more emphases on the techniques of outlier detection for the complicated data with highdimensionality. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc. Outlier detection for temporal data synthesis lectures. Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, timeseries data, discrete sequence data, spatial data, and network data. In contrast to other applications, this particular domain includes a very dynamic dimension. Timedependent popular routes based trajectory outlier. Outlier detection for temporal data by jing gao, manish gupta. Initial research in outlier detection focused on time series based outliers in statistics. It starts with the basic topics then moves on to state of the art techniques in the field. This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting reallife problems.
Outlier analysis springer authored by charu aggarwal, 2017. Anomaly detection for timeseries data has been an important. Spatio temporal outlier detection plays an important role in some applications. Most of the previous books on outlier detection were written by statisticians for statisticians, with little or no coverage from the data mining and computer science perspective. Outliers are calculated from drastic changes in the trends. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal. Outlier detection or anomaly detection is a fundamental task in data mining. A number of outlier detection methods have been developed which regard an outlier as a record which falls in an unlikely region based on the local or overall statistical properties of the data. A univariate time series is transformed to bivariate data. Unsupervised anomaly detection in multivariate spatio. Outlier or anomaly detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio temporal mining, etc. Outlier detection for temporal data microsoft research. Anomaly detection of time series university of minnesota.
In particular, advances in hardware technology have enabled the availability of. In this paper, a multivariate outlier detection algorithm is given to detect outliers in time series models. May 23, 2014 outlier or anomaly detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio temporal mining, etc. Detecting anomalies in spatial time series data using spatio temporal clustering is a novel idea proposed in this study. Unsupervised anomaly detection in multivariate spatiotemporal. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel. Oct 09, 2019 manish gupta, jing gao, charu aggarwal, outlier detection for temporal data 2014 pages. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. Initial research in outlier detection focused on time seriesbased outliers in statistics. A large number of applications generate temporal datasets. Outlier detection for temporal data synthesis lectures on data.
Outlier detection is one of the major data mining methods. Abstractin the statistics community, outlier detection for time series data has. We also discuss the methods of the spatial outlier detection and temporal outlier detection before the main content for the aim of having a better understand of data outlier detection field. Outlier detection algorithms vary in the way the time series is represented. Outlier detection an overview sciencedirect topics. Jul, 2019 therefore, outlier detection is one of the most important preprocessing steps in any data analytical application 1114. Part of the lecture notes in computer science book series lncs, volume. In outlier detection, the hampel identifier hi is the most widely used and efficient outlier identifier 15. Dealing with temporal data in time series datasets, the assumption of temporal continuity plays an important role in defining and detecting outliers. In this paper, we place more emphases on the techniques of outlier detection for the complicated data.
Outlier detection for temporal data analyticbridge. Outlier detection for temporal data covers topics in temporal outlier detection, which have applications in numerous fields. Challenges for outlier detection for temporal data definition of outliers such that it captures properties of the data properties of the network space and time dimensions massive scale data trend drift detection and handling time efficient, single scan distributed data streams. Outlier detection for temporal data synthesis lectures on data mining and knowledge discovery, band 8 gupta, manish, gao, jing, aggarwal, charu, han, jiawei isbn. Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. Key components associated with outlier detection technique. In this study, csb data collected from 2012 to 20 within the baltimore inner harbor was used to locate anomalous depth measurements that could indicate the presence of submerged debris. In this tutorial, we will present an organized picture of recent research in temporal outlier detection. A comparison of outlier detection techniques for high. Outlier detection for temporal data manish gupta, microsoft india and iiit. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. In one such approach, upper and lower percentile thresholds as set as the outlier cutoff based on the interquartile range laurikkala et al. Sep 20, 2019 although many temporal data are spatio temporal in nature, existing techniques are limited to handle both contextual spatial and temporal attributes during anomaly detection process.
Download bibtex in the statistics community, outlier detection for time series data has been studied for decades. One application of spatio temporal data mining is spatiotemporal outlier detection. This has stimulated many researchers in both temporal and spatial outlier detection 1519. When analyzing single time series, the lack of temporal continuity with immediate neighbors signal outliers. The results demonstrate that our techniques outperform the state. Army research laboratory under cooperative agreement w911nf1120086 cybersecurity and cooperative agreement w911nf0920053 nscta, in part by the u. Compared to general outlier detection, techniques for temporal outlier detection are very di. Accuracy of outlier detection depends on how good the clustering algorithm captures the structure of clusters a t f b l d t bj t th t i il t h th lda set of many abnormal data objects that. Outlier detection is often complicated by noise in the data, so a good outlier detection methodology should be successful in identifying outliers in noisy data. Detecting spatiotemporal outliers in crowdsourced bathymetry. Outliers are data that deviate from the norm and outlier detection is often.
Recently, with advances in hardware and software technology, there has been a large body. For each group of trajectories with the same source and destination, we firstly design a timedependent transfer graph and in different time period, we can obtain the topk most popular routes as reference. A survey in the statistics community, outlier detection for time series data has been studied for decades. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points.
Global outlier methods calculate a single outlier statistic that summarizes the outliers for the entire geographic area and temporal duration, while local outlier methods calculate a. Outlier detection for temporal data by manish gupta, jing. To this end, we explored two approaches for detecting spatio temporal outliers in the csb data. Noise in the data which tends to be similar to the actual outliers and hence difficult to distinguish and remove. However, the outliers may generate high value if they are found, value in terms of cost savings, im. In this section, a novel approach called the extensible markov mode. The manufacturing process and its cyberphysical systems cpss produce large amounts of data with many relationships and dependen. Outlier detection for temporal data by manish gupta, jing gao. The others mainly focuses on either specifical applications, such as network data 4 and temporal data 5, or particular learning techniques, such as subspace learning and ensemble learning 6. This book highlights several methodologies for detection of outliers with a special focus on categorical data and sheds light on certain stateoftheart algorithmic approaches such as communitybased analysis of networks and characterization of temporal outliers present in dynamic networks. Since then, outlier detection has been studied on a large variety of data types including highdimensional data, uncertain data, stream data. The effectiveness of our methods is justified by empirical results on real data sets.
This paper proposes a threestep approach to detect spatiotemporal outliers in large databases. Comprehensive text book on outlier analysis, including examples and exercises for classroom teaching. Spatio temporal outlier detection methods in recent two decades and list, explain two most useful algorithms to stod. In the statistics community, outlier detection for time series data has. Due to the above challenges, the outlier detection problem, in its most general form, is not easy to solve. Since then, outlier detection has been studied on a large variety of data types including highdimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatiotemporal data. Since then, outlier detection has been studied on a large variety of data. Spatiotemporal outlier detection in large databases ieee. Many existing algorithms have studied the problem of outlier detection at a single instant in time. Compared to general outlier detection, techniques for temporal outlier detection are very different. Temporal and spatial outlier detection in wireless sensor. In particular, advances in hardware technology have enabled the availability of various forms of. In the statistics community, outlier detection for time series data has been studied for decades. Accuracy of outlier detection depends on how good the clustering algorithm captures the structure of clusters a t f b l d t bj t th t i il t h th lda set of many abnormal data objects that are similar to each other would.
Timedependent popular routes based trajectory outlier detection. Nov 01, 2015 in our algorithm, spatial and temporal abnormalities are taken into consideration simultaneously to improve the accuracy of the detection. Outliers are data that deviate from the norm and outlier detection is often compared to finding a needle in a haystack. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book.
The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. Chapter is devoted to various applications of outlier analysis. Outlier detection for temporal data by jing gao, manish. Outlier detection in temporal spatial log data using. Keywords gaussian process, hampel identifier, outlier detection, spatial outlier, temporal outlier. Outlier detection for temporal data synthesis lectures on. Apr 14, 2014 outlier or anomaly detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio temporal mining, etc. This has fueled the growth of different kinds of data sets such as data streams, spatiotemporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. Adapted knearest neighbors for detecting anomalies on. Outlier or anomaly detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatiotemporal mining, etc. Most of the real world time series datasets have spatial dimension as additional context such as geographic location. Depending on this understanding we propose a novel transformation technique for univariate time series which uses subspace based analysis. An algorithm for outlier detection in a time series model using. In this section, a novel approach called the extensible markov model emm35 is presented.
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