Dynamic graph embedding for outlier detection on multiple meteorological time series

Li, Gen; Jung, Jason J.

doi:10.1371/journal.pone.0247119

Cited by 13 publications

(4 citation statements)

References 41 publications

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“…In a prior work (Li & Jung, 2021a, 2021b), we presented an approach for computing the spurious correlation coefficient using the Granger causal test and Pearson correlation (Deng et al, 2021), which is described as follows.Definition (Spurious relationship). The spurious relationship between two time series is described as two correlated time series that lack causation, which is represented as

R (x, y) = \{\begin{matrix} left \\ 1 & if C (x, y) = 0 \\ C (x, y) - |PCC (x, y)| & otherwise \end{matrix}

where C ( x , y ) and PCC reflect the causal relationship and correlation between the two data streaming, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Dynamic graph embedding‐based anomaly detection on internet of things time series

Jung

2022

Expert Systems

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Anomaly detection is critical in the internet of things (IoT) environment. To this issue, this study provides a novel approach for detecting anomalies in multivariate IoT time series. The proposed approach identified relationships between IoT time series to establish a dynamic graph and estimated the graph entropy to detect anomalies. The presented approach was applied to industrial IoT datasets. The results have shown that the presented method outperformed other models by 0.21 with respect to F1‐score. In addition, we used three distinct algorithms to detect the anomalies from the multivariate IoT time series. According to the results, the local outlier factor approach outperformed the others by 0.18 with respect to F1‐score.

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R (x, y) = \{\begin{matrix} left \\ 1 & if C (x, y) = 0 \\ C (x, y) - |PCC (x, y)| & otherwise \end{matrix}

where C ( x , y ) and PCC reflect the causal relationship and correlation between the two data streaming, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In a prior work (Li & Jung, 2021a, 2021b, we presented an approach for computing the spurious correlation coefficient using the Granger causal test and Pearson correlation (Deng et al, 2021), which is described as follows.…”

Section: Dynamic Graph Constructionmentioning

confidence: 99%

Dynamic graph embedding‐based anomaly detection on internet of things time series

Jung

2022

Expert Systems

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“…Then, the network is used to extract sudden fluctuations (anomalies) in EEG time series. There are some graph-based outlier detection methods that map a time series to a graph by discovering relationships [ 38 ]. A method based on a time series graph representation is developed in [ 39 ] to detect outliers.…”

Section: Related Workmentioning

confidence: 99%

GraphTS: Graph-represented time series for subsequence anomaly detection

Zarei

Huang

2023

PLoS ONE

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Automatic detection of subsequence anomalies (i.e., an abnormal waveform denoted by a sequence of data points) in time series is critical in a wide variety of domains. However, most existing methods for subsequence anomaly detection often require knowing the length and the total number of anomalies in time series. Some methods fail to capture recurrent subsequence anomalies due to using only local or neighborhood information for anomaly detection. To address these limitations, in this paper, we propose a novel graph-represented time series (GraphTS) method for discovering subsequence anomalies. In GraphTS, we provide a new concept of time series graph representation model, which represents both recurrent and rare patterns in a time series. Particularly, in GraphTS, we develop a new 2D time series visualization (2Dviz) method, which compacts all 1D time series patterns into a 2D spatial temporal space. The 2Dviz method transfers time series patterns into a higher-resolution plot for easier sequence anomaly recognition (or detecting subsequence anomalies). Then, a Graph is constructed based on the 2D spatial temporal space of time series to capture recurrent and rare subsequence patterns effectively. The represented Graph also can be used to discover single and recurrent subsequence anomalies with arbitrary lengths. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency.

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“…To solve this problem, we propose to construct a dynamic graph by discovering the correlation between the meteorological time series in the previous research 5 . The idea mainly consists of three steps.…”

Section: Introductionmentioning

confidence: 99%

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

Jung

2021

Sci Rep

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Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal climate events utilize supervised learning models to learn the abnormal patterns, but they cannot detect the untrained patterns. 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 to discriminate anomalies. The graph entropy measurement quantifies the information of the graphs and constructs the embedding space. We conducted experiments on synthetic datasets and real-world meteorological datasets. The results showed that EdynGE model achieved a better F1-score than the baselines by 43.2%, and the number of days of abnormal climate events has increased by 304.5 days in the past 30 years.

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Dynamic graph embedding for outlier detection on multiple meteorological time series

Cited by 13 publications

References 41 publications

Dynamic graph embedding‐based anomaly detection on internet of things time series

Dynamic graph embedding‐based anomaly detection on internet of things time series

GraphTS: Graph-represented time series for subsequence anomaly detection

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

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