Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach

Izakian, Hesam; Pedrycz, Witold

doi:10.1109/tfuzz.2014.2302456

Cited by 87 publications

(27 citation statements)

References 34 publications

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“…Spatial time series is an important type of data in many fields [12], such as, economy [25], global trade [27], traffic [35] and emergency response [33]. Research has been focusing on situation understanding [9], forecasting [29], anomaly detection [28], and relationship identifying among different parts of data [59]. The proposed methods have acknowledged limitations [23]; thus, they lack the capability to display the results in a form that is easily perceived by humans and to involve human analysts in the analysis process.…”

Section: Analysis Of Spatial Time Series and Eventsmentioning

confidence: 99%

COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Chen

Zhang³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.

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Section: Analysis Of Spatial Time Series and Eventsmentioning

confidence: 99%

COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Chen

Zhang³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…It models behaviour but it does so in a short time window such as one hour. Izakian et al [37] considers Fuzzy C-means (FCM) as a conceptual and algorithmic setting to deal with the problem of anomaly detection. Their work is also based on small size of time series which contains only 10 data points.…”

Section: Related Workmentioning

confidence: 99%

Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

Zhao

Japkowicz

2018

J Big Data

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IntroductionAnomaly detection for analysing spatio-temporal data remains a rapidly growing problem in the wake of an ever-increasing number of advanced sensors that are continuously generating large-scale datasets. For example, vehicle GPS tracking, social media, financial network and router logs, and high resolution surveillance cameras all generate a huge amount of spatio-temporal data. This technology is also important in the context of cyber security since cyber data carries with it an IP address which can map to a specific geolocation and a timestamp. Yet, current cybersecurity approaches are not able to process this kind of information effectively. To illustrate this deficiency, consider the scenario of a distributed denial-of-service (DDoS) attack in which the network packets may come from different IP addresses with sparse locations. In such a case, a spatio-temporal analyzing system [1] is required to analyse the spatial pattern of the DDoS attack. Yet, user oriented analytic environments for cyber security with spatio-temporal marks are currently limited to traditional statistical methods like spatial-temporal outlier detection and hotspot detection [2]. 1 Furthermore, much of the current work in large scale AbstractIn this paper, we propose a framework for processing and analysing large-scale spatiotemporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.

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“…1) Sliding window approach: Given a continuous time series data Q, the sliding window technique running along the time axis depends on two significant parameters which are window size W and stride (offset) S. Sliding Window is also called a brute force or one-pass algorithm [25] and has been used in many time series works [12], [13], [15], [29]. It is an appropriate way to deal with temporal data because it sequentially processes the raw data keeping into account its temporal behavior.…”

Section: A Preprocessingmentioning

confidence: 99%

Towards Visual Exploration of Large Temporal Datasets

Ali

Jones

Xie

et al. 2018

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)

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We address the problem of visualizing and interacting with large multi-dimensional time-series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.

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Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach

Cited by 87 publications

References 34 publications

COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

Towards Visual Exploration of Large Temporal Datasets

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