Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Braei, Mohammad; Wagner, Sebastian

doi:10.48550/arxiv.2004.00433

Cited by 52 publications

(71 citation statements)

References 53 publications

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“…To the best of our knowledge, we are the first to evaluate on anomaly detection [Su et al, 2019, Goh et al, 2016, Mathur and Tippenhauer, 2016, Braei and Wagner, 2020. The results of this task assessment reflect how well the model capture the temporal trends and how sensitive to the outlier the model is for time series.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Yang¹,

Hong²

2022

Preprint

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Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the framework of contrastive learning with the time-based augmentation techniques to sample positives and negatives for contrastive training. Nevertheless, they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context. Besides, they all pay no attention to incorporate the spectral information in feature representation. In this paper, we propose a unified framework, namely Bilinear Temporal-Spectral Fusion (BTSF). Specifically, we firstly utilize the instance-level augmentation with a simple dropout on the entire time series for maximally capturing long-term dependencies. We devise a novel iterative bilinear temporal-spectral fusion to explicitly encode the affinities of abundant time-frequency pairs, and iteratively refines representations in a fusion-and-squeeze manner with Spectrum-to-Time (S2T) and Time-to-Spectrum (T2S) Aggregation modules. We firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection. Experimental results shows that our BTSF consistently significantly outperforms the state-of-the-art methods.

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Section: Anomaly Detectionmentioning

confidence: 99%

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Yang¹,

Hong²

2022

Preprint

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“…Time-series anomaly detection is an active research topic that has been largely studied (Aggarwal 2016;Chandola, Banerjee, and Kumar 2009;Chalapathy and Chawla 2019;Gupta et al 2013;Braei and Wagner 2020). Traditional approaches of anomaly detection mainly focus on utilizing prior assumptions on normal patterns which can be categorized into distance-based (Ramaswamy, Rastogi, and Shim 2000;Chaovalitwongse, Fan, and Sachdeo 2007), densitybased (Breunig et al 2000;Ma and Perkins 2003), isolationbased (Liu, Ting, and Zhou 2008) and statistic-based (Siffer et al 2017;Li et al 2020) methods.…”

Section: Related Workmentioning

confidence: 99%

Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction

Xiao¹,

Shao²,

Wang³

2021

Preprint

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Detecting anomalies for multivariate time-series without manual supervision continues a challenging problem due to the increased scale of dimensions and complexity of today's IT monitoring systems. Recent progress of unsupervised time-series anomaly detection mainly use deep autoencoders to solve this problem, i.e. training on normal samples and producing significant reconstruction error on abnormal inputs. However, in practice, autoencoders can reconstruct anomalies so well, due to powerful capabilites of neural networks. Besides, these approaches can be ineffective for identifying non-point anomalies, e.g. contextual anomalies and collective anomalies, since they solely utilze a pointwise reconstruction objective. To tackle the above issues, we propose MemAAE (Memory-augmented Adversarial Autoencoders with Deep Reconstruction and Prediction), a novel unsupervised anomaly detection method for time-series. By jointly training two complementary proxy tasks, reconstruction and prediction, with a shared network architecture, we show that detecting anomalies via multiple tasks obtains superior performance rather than single-task training. Additionally, a compressive memory module is introduced to preserve normal patterns, avoiding unexpected generalization on abnormal inputs. Through extensive experiments, MemAAE achieves an overall F1 score of 0.90 on four public datasets, significantly outperforming the best baseline by 0.02.

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“…Anomaly detection has been studied and applied to time series data for decades [4]. Anomaly detection is cast into the three paradigms based on the nature of the data capturing anomalies; supervised, semi-supervised and unsupervised approaches [1].…”

Section: Related Workmentioning

confidence: 99%

Time Series Anomaly Detection with label-free Model Selection

Jung,

Ramanan,

Amjadi

et al. 2021

Preprint

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Anomaly detection for time-series data becomes an essential task for many datadriven applications fueled with an abundance of data and out-of-the-box machinelearning algorithms. In many real-world settings, developing a reliable anomaly model is highly challenging due to insufficient anomaly labels and the prohibitively expensive cost of obtaining anomaly examples. It imposes a significant bottleneck to evaluate model quality for model selection and parameter tuning reliably. As a result, many existing anomaly detection algorithms fail to show their promised performance after deployment. In this paper, we propose LaF-AD, a novel anomaly detection algorithm with labelfree model selection for unlabeled times-series data. Our proposed algorithm performs a fully unsupervised ensemble learning across a large number of candidate parametric models. We develop a model variance metric that quantifies the sensitivity of anomaly probability with a bootstrapping method. Then it makes a collective decision for anomaly events by model learners using the model variance. Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models. We evaluate our algorithm against other state-of-the-art methods on a synthetic domain and a benchmark public data set.Preprint. Under review.

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Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Cited by 52 publications

References 53 publications

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction

Time Series Anomaly Detection with label-free Model Selection

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