Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles.

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With the sweeping digitalization of societal, medical, industrial,
and scientific processes, sensing technologies are being deployed
that produce increasing volumes of time series data, thus fueling a
plethora of new or improved applications. In this setting, outlier
detection is frequently important, and while solutions based on
neural networks exist, they leave room for improvement in terms
of both accuracy and efficiency. With the objective of achieving
such improvements, we propose a diversity-driven, convolutional
ensemble. To improve accuracy, the ensemble employs multiple
basic outlier detection models built on convolutional sequence-tosequence autoencoders that can capture temporal dependencies in
time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving
the ensemble’s accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it
is able to transfer some model parameters from one basic model
to another, which reduces training time. We report on extensive
experiments using real-world multivariate time series that offer
insight into the design choices underlying the new approach and
offer evidence that it is capable of improved accuracy and efficiency.
Original languageEnglish
JournalProceedings of the VLDB Endowment
Issue number3
Pages (from-to)611-623
Publication statusPublished - 2021


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