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Anomaly detection in time series is a complex task that has been widely studied.
Control procedures for residuals associated with principal component analysis,
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A multivariate exponentially weighted moving average control chart,
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A density-based algorithm for discovering clusters in large spatial databases with noise,
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B. Xia, Similarity search in time series data sets, Ph.D. thesis, Simon Fraser University, 1997
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Lof: identifying density-based local outliers,
M. M. Breunig, H.-P. Kriegel, R. T. Ng, J. Sander, · 2000
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Statistical modeling: The two cultures,
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Estimating support of a high-dimensional distribution,
B. Schölkopf, J. Platt, J. Shawe-Taylor, A. Smola, R. Williamson, · 2001
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M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, L. Chang, A novel anomaly detection scheme based on principal component classifier, Technical Report, University of Miami, 2003
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Time-series novelty detection using one-class support vector machines,
J. Ma, S. Perkins, · 2003
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Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation,
X. Pan, J. Jarrett, · 2007
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Isolation forest,
F. T. Liu, K. M. Ting, Z.-H. Zhou, · 2008
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Anomaly detection: A survey,
V. Chandola, A. Banerjee, V. Kumar, · 2009
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Multi-dimensional traffic anomaly detection based on ica,
L. Zonglin, H. Guangmin, Y. Xingmiao, · 2009
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N. Golyandina, A. Zhigljavsky, Singular Spectrum Analysis for Time Series, 2013
2013
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Deep learning,
Y. LeCun, Y. Bengio, G. Hinton, · 2015
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Multivariate outlier detection using independent component analysis,
M. Reza, · 2015
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Event detection in marine time series data,
S. Oehmcke, O. Zielinski, O. Kramer, · 2015
Cited alongside, same era.
Matrix profile i: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets,
C.-C. M. Yeh, Y. Zhu, L. Ulanova, N. Begum, Y. Ding, H. A. Dau, D. F. Silva, A. Mueen, E. Keogh, · 2016
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Why deep-learning ais are so easy to fool,
D. Heaven, · 2019
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Usad: Unsupervised anomaly detection on multivariate time series,
J. Audibert, P. Michiardi, F. Guyard, S. Marti, M. A. Zuluaga, · 2020
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R. Wu, E. J. Keogh, Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress, 2020
2020
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On instabilities of deep learning in image reconstruction and the potential costs of AI,
V. Antun, F. Renna, C. Poon, B. Adcock, A. C. Hansen, · 2020
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Does deep learning always outperform simple linear regression in optical imaging?,
S. Jiao, Y. Gao, J. Feng, T. Lei, X. Yuan, · 2020
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The m4 competition: 100,000 time series and 61 forecasting methods,
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A comparative evaluation of outlier detection algorithms: Experiments and analyses,
R. Domingues, M. Filippone, P. Michiardi, J. Zouaoui, · 2018
Cited alongside, same era.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection,
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, H. Chen, · 2018
Cited alongside, same era.
The m4 competition: Results, findings, conclusion and way forward,
S. Makridakis, E. Spiliotis, V. Assimakopoulos, · 2018
Cited alongside, same era.
A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder,
D. Park, Y. Hoshi, C. C. Kemp, · 2018
Cited alongside, same era.
Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications,
H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al., · 2018
Cited alongside, same era.
Matrix profile xi: Scrimp++: time series motif discovery at interactive speeds,
Y. Zhu, C.-C. M. Yeh, Z. Zimmerman, K. Kamgar, E. Keogh, · 2018
Cited alongside, same era.
S. Makridakis, E. Spiliotis, V. Assimakopoulos, · 2020
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Matrix profile goes mad: variable-length motif and discord discovery in data series,
M. Linardi, Y. Zhu, T. Palpanas, E. Keogh, · 2020
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Anomaly detection for time series using vae-lstm hybrid model,
S. Lin, R. Clark, R. Birke, S. Schönborn, N. Trigoni, S. Roberts, · 2020
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Assessment of dispersion patterns for negative stress detection from electroencephalographic signals,
B. García-Martínez, A. Fernández-Caballero, R. Alcaraz, A. Martínez-Rodrigo, · 2021
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Peer-reviewed deep learning-based methods for anomaly detection in multivariate time series from 2018 to 2021 (2021). URL: https://www.doi.org/10.6084/m9.figshare.16536144.v1
J. Audibert, · 2021
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“keep it simple, scholar”: an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging,
W. Fu, K. Breininger, R. Schaffert, Z. Pan, A. Maier, · 2021
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An evaluation of anomaly detection and diagnosis in multivariate time series,
A. Garg, W. Zhang, J. Samaran, R. Savitha, C.-S. Foo, · 2021
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Graph-based stock correlation and prediction for high-frequency trading systems,
T. Yin, C. Liu, F. Ding, Z. Feng, B. Yuan, N. Zhang, · 2022
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