Abstract
As industries become automated and connectivity technologies advance, a wide range of systems continues to generate massive amounts of data. Many approaches have been proposed to extract principal indicators from the vast sea of data to represent the entire system state. Detecting anomalies using these indicators on time prevent potential accidents and economic losses. Anomaly detection in multivariate time series data poses a particular challenge because it requires simultaneous consideration of temporal dependencies and relationships between variables. Recent deep learning-based works have made impressive progress in this field. They are highly capable of learning representations of the large-scaled sequences in an unsupervised manner and identifying anomalies from the data. However, most of them are highly specific to the individual use case and thus require domain knowledge for appropriate deployment. This review provides a background on anomaly detection in time-series data and reviews the latest applications in the real world. Also, we comparatively analyze state-of-the-art deep-anomaly-detection models for time series with several benchmark datasets. Finally, we offer guidelines for appropriate model selection and training strategy for deep learning-based time series anomaly detection.
Highlights
E VERYTHING on the Earth is a source of signals
In the temporal hierarchical one-class (THOC) network [60] and Temporal Convolutional Network (TCN)-Gaussian mixture model (GMM) [117], time-series features are extracted by a dilated recurrent neural networks (RNN) and TCN, respectively
Secure Water Treatment (SWaT) [57]: multi-variate time-series data collected over 11 days from water treatment test-bed, a small-scale cyber-physical system
Summary
E VERYTHING on the Earth is a source of signals. Humans have continuously measured and collected signals occurring in nature, such as temperature, wind speed, rainfall, and sunspot intensity, to adapt to the environment. Min et al [7] proposed a novel computational method using a recurrence plot (RP), a square matrix consisting of the times at which a state of a dynamic system recurs They measure the local recurrence rates (LREC) by scanning the RP with a sliding window and detect anomalies by comparing similarities between the statistics of the LREC curves. Ke et al [8] proposed to combine ensembled long short term memory (LSTM) neural networks, which memorize long term patterns in time series, with the stationary wavelet transform (SWT), to forecast the energy consumption Their experimental results showed that the proposed deep-learning method outperforms classical computational methods. The goal of this study is to review state-of-the-art deep learning-based anomaly detection methods for time-series data.
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