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A Review on Anomaly Detection in Time Series
2021
International Journal of Advanced Trends in Computer Science and Engineering
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In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series ...
Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research ...
Deep Learning Approach for Unsupervised Outlier Detection in Time Series In standard anomaly detection techniques, current points and seasonal fluctuations normally found in streaming data cannot be observed ...
doi:10.30534/ijatcse/2021/571032021
fatcat:5alfes5srzakzdvfgxtdniyn3q
FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models
2019
Sensors
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In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. ...
For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. ...
and deep learning models for anomaly detection. ...
doi:10.3390/s19112451
fatcat:pefz2ibc5zb67c2biexmyfustu
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
2020
IEEE Access
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In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. ...
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. ...
[66] present DeepAnT, a deep learning based unsupervised anomaly detection approach for time series data. ...
doi:10.1109/access.2020.3022366
pmid:34931155
pmcid:PMC8668158
fatcat:m23b5ijr25ahfko2ktxqhx2fsa
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks
[article]
2019
arXiv
pre-print
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In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. ...
The proposed OeSNN-UAD approach was experimentally compared to the state-of-the-art unsupervised methods and algorithms for anomaly detection in stream data. ...
Abstract-In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. ...
arXiv:1912.08785v1
fatcat:guh5dcypdbdn5fi2idtfsfvqza
KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications
2019
Symmetry
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To address this limitation, we propose a novel anomaly detector (called KPI-TSAD) for time-series KPIs based on supervised deep-learning models with convolution and long short-term memory (LSTM) neural ...
Therefore, anomaly detection presents a challenge for all types of temporal data, particularly when non-stationary time series have special adaptability requirements or when the nature of potential anomalies ...
We give thanks to the public datasets provided by the Yahoo and AIOps Challenges; those datasets provided strong support for our experiments. ...
doi:10.3390/sym11111350
fatcat:svpgcv3ljzhvflj4m5b6p6k5gq
Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error
2020
Symmetry
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By focusing on these problems, we propose a novel deep learning-based unsupervised anomaly detection approach (RE-ADTS) for time-series data, which can be applicable to batch and real-time anomaly detections ...
Automatic anomaly detection for time-series is critical in a variety of real-world domains such as fraud detection, fault diagnosis, and patient monitoring. ...
Mohsin et al. presented a deep learning-based DeepAnT method for unsupervised anomaly detection in time-series [20] . ...
doi:10.3390/sym12081251
fatcat:bm5cjz7775f5tnmjjfphvdldca
Change Point Enhanced Anomaly Detection for IoT Time Series Data
2021
Water
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We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. ...
In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. ...
Another unsupervised non-Deep Learning approach is PBAD (pattern-based anomaly detection) [27] . ...
doi:10.3390/w13121633
fatcat:mji72vblmjcwrpl3vtse7pbhny
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
[article]
2020
arXiv
pre-print
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Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. ...
In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. ...
Deep Learning Anomaly detection approaches on time series Munir et al. ...
arXiv:2004.00433v1
fatcat:6ang6kfgsrd7jpck5ec4u6shoa
Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles
2022
Computers Materials & Continua
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This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities in metro train braking systems. ...
The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. ...
The authors in [30] introduced a model called DeepAnT for time-series anomaly detection in an unsupervised manner. ...
doi:10.32604/cmc.2022.023641
fatcat:arwxb6onvbehtil4plhu4h4r2e
Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
2021
Applied Sciences
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Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. ...
However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. ...
In recent years, various unsupervised approaches for anomaly detection have been proposed and categorized into traditional machine learning and deep learning approaches. ...
doi:10.3390/app11199290
fatcat:p3jhfa7vpjek3ma2xoldjjmxd4
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
[article]
2020
arXiv
pre-print
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With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. ...
Furthermore, this model retains the advantages of LSTM unit in predicting time series data. ...
Munir et al. in [15] proposed a novel DAD approach, called DeepAnT, to achieve anomaly detection by utilizing deep Convolutional Neural Network (CNN) to predict anomaly value. ...
arXiv:2007.09712v1
fatcat:tkfhoj4rmrhnjmgp5zyn33xlai
Warping Resilient Scalable Anomaly Detection in Time Series
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel unsupervised time series anomaly detection method, WaRTEm-AD, that operates in two stages. ...
for time series data. ...
The work is supported by project titled "Robust Multiview Learning for Extreme Events Detection and Prediction in Time Series Data", IITPKD/2021/013/CSE/SAB funded by ICSR ...
arXiv:1906.05205v2
fatcat:yse2nf5d45akzdytutkd63lf5i
A simple method for unsupervised anomaly detection: An application to Web time series data
2022
PLoS ONE
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We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based ...
The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. ...
Acknowledgments We are grateful to Flat inc. for sharing the data set with us. We also thank the participants at the International Conference on Operations Research 2019 for their helpful comments. ...
doi:10.1371/journal.pone.0262463
pmid:35015791
pmcid:PMC8752013
fatcat:bekblaruy5bmdo6x2vasnipesm
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
[article]
2021
arXiv
pre-print
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This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using ...
Furthermore, detecting anomalies in multivariate time series is difficult due to their temporal dependency and stochasticity. ...
DeepAnt [31] was an unsupervised approach using convolutional neural network (CNN) to forecast future time series values and adopted Euclidean distance to measure the discrepancy for anomaly detection ...
arXiv:2104.03466v2
fatcat:kbiv3vvfmrbfhdzstdxdyz5xva
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
[article]
2020
arXiv
pre-print
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This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. ...
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series. ...
Model The backbone of our anomaly detection technique is a deep probabilistic distributional time series model, i.e. a probabilistic model for time series x 1:T = x 1 , x 2 , . . . , x T , where each element ...
arXiv:2007.15541v1
fatcat:bx2tzzq5zrb3ro6pctbg6g6rty
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