TSLOD: a coupled generalized subsequence local outlier detection model for multivariate time series

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Entropy ◽

10.3390/e23111466 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1466

Author(s):

Kamil Faber ◽

Marcin Pietron ◽

Dominik Zurek

Keyword(s):

Time Series ◽

Deep Learning ◽

Anomaly Detection ◽

Multivariate Time Series ◽

Window Size ◽

Learning Models ◽

Failure Prevention ◽

Detection Model ◽

Repair Costs ◽

Fully Automatic

Multivariate time series anomaly detection is a widespread problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms that automate the process of detecting anomalies are crucial in modern failure prevention systems. Therefore, many machine learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. This work shows a framework that incorporates neuroevolution methods to boost the anomaly detection scores of new and already known models. The presented approach adapts evolution strategies for evolving an ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimize the architecture and hyperparameters such as the window size, the number of layers, and the layer depths. The proposed framework shows that it is possible to boost most anomaly detection deep learning models in a reasonable time and a fully automated mode. We ran tests on the SWAT and WADI datasets. To the best of our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.

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Two-phase outlier detection in multivariate time series

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2011.6019794 ◽

2011 ◽

Cited By ~ 1

Author(s):

Xin Wang

Keyword(s):

Time Series ◽

Outlier Detection ◽

Multivariate Time Series ◽

Two Phase

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Outlier Detection in Multivariate Time Series by Projection Pursuit

Journal of the American Statistical Association ◽

10.1198/016214505000001131 ◽

2006 ◽

Vol 101 (474) ◽

pp. 654-669 ◽

Cited By ~ 54

Author(s):

Pedro Galeano ◽

Daniel Peña ◽

Ruey S Tsay

Keyword(s):

Time Series ◽

Outlier Detection ◽

Multivariate Time Series ◽

Projection Pursuit

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Dynamic graph embedding for outlier detection on multiple meteorological time series

PLoS ONE ◽

10.1371/journal.pone.0247119 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0247119

Author(s):

Gen Li ◽

Jason J. Jung

Keyword(s):

Time Series ◽

Outlier Detection ◽

Meteorological Data ◽

Graph Embedding ◽

Detection Methods ◽

Dynamic Graph ◽

Local Outlier Factor ◽

Box Plot ◽

Local Outlier ◽

Isolation Forest

Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. 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 graph proximity is described as the distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.

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