Neural Contextual Anomaly Detection for Time Series

Carmona, Chris U.; Aubet, François-Xavier; Flunkert, Valentín; Gasthaus, Jan

doi:10.48550/arxiv.2107.07702

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…In addition, the IR health monitoring task is essentially an MTSAD problem, while classification-based AD methods [25,26] face the challenge of data imbalance. Therefore, some research takes a process of augmentation and uses the labels [27][28][29][30], while some unrealistic anomalies may be introduced in studies. Normality-based anomaly detection methods [31][32][33][34][35][36] believe that normal data should have one or more characteristics, while data that do not meet them will be judged as abnormal.…”

Section: Related Workmentioning

confidence: 99%

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

Jiang,

Hu,

Liu

et al. 2024

Electronics

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The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.

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Section: Related Workmentioning

confidence: 99%

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

Jiang,

Hu,

Liu

et al. 2024

Electronics

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“…In [17], the authors used variants of SSAD model for benchmarking and found that there is no one-fit-all strategy for one-class active learning. Recently, Amazon developed NCAD based on deep semi-supervised learning [14] for time series anomaly detection [3].…”

Section: Related Workmentioning

confidence: 99%

“…It should be noted that NCAD [3] does not strictly follow the adopted evaluation protocol. It uses a more relaxed one without a delay restriction, which is thus equivalent to the adopted protocol with k = ∞.…”

Section: Supervised and Unsupervised Anomaly Detectionmentioning

confidence: 99%

Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

Bodor¹,

Hoang²,

Jiang³

2022

Preprint

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Key Performance Indicators (KPI), which are essentially time series data, have been widely used to indicate the performance of telecom networks. Based on the given KPIs, a large set of anomaly detection algorithms have been deployed for detecting the unexpected network incidents. Generally, unsupervised anomaly detection algorithms gain more popularity than the supervised ones, due to the fact that labeling KPIs is extremely time-and resource-consuming, and error-prone. However, those unsupervised anomaly detection algorithms often suffer from excessive false alarms, especially in the presence of concept drifts resulting from network re-configurations or maintenance. To tackle this challenge and improve the overall performance of unsupervised anomaly detection algorithms, we propose to use active learning to introduce and benefit from the feedback of operators, who can verify the alarms (both false and true ones) and label the corresponding KPIs with reasonable effort. Specifically, we develop three query strategies to select the most informative and representative samples to label. We also develop an efficient method to update the weights of Isolation Forest and optimally adjust the decision threshold, so as to eventually improve the performance of detection model. The experiments with one public dataset and one proprietary dataset demonstrate that our active learning empowered anomaly detection pipeline could achieve performance gain, in terms of F1-score, more than 50% over the baseline algorithm. It also outperforms the existing active learning based methods by approximately 6% − 10%, with significantly reduced budget (the ratio of samples to be labeled).

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“…A unified contrastive anomaly detection framework is proposed by [25]. Carmona et al perform time series anomaly detection by generating abnormal series with expertise knowledge [26]. Qiu et al propose deterministic contrastive loss to enable the anomaly score to be consistent with training loss [27].…”

Section: Unsupervised Anomaly Detectionmentioning

confidence: 99%

Deep Federated Anomaly Detection for Multivariate Time Series Data

Zhu¹,

Song²,

Chen³

et al. 2022

Preprint

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Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplarbased Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-ofthe-art anomaly detection algorithms and federated learning techniques.

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Neural Contextual Anomaly Detection for Time Series

Cited by 4 publications

References 24 publications

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

Deep Federated Anomaly Detection for Multivariate Time Series Data

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