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Anomaly detection in time series data is a critical challenge across various domains.
Lof: identifying density-based local outliers
Breunig, M. M., H.-P. Kriegel, R. T. Ng, et al · 2000
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Anomaly detection in time series of graphs using arma processes
Pincombe, B · 2005
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Unsupervised outlier detection in time series data
Ferdousi, Z., A. Maeda · 2006
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ECG anomaly detection via time series analysis
Chuah, M. C., F. Fu · 2007
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Traffic anomaly detection using k-means clustering
Münz, G., S. Li, G. Carle · 2007
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Isolation forest
Liu, F. T., K. M. Ting, Z.-H. Zhou · 2008
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Modeling events with cascades of poisson processes, 2010
Simma, A., M. I. Jordan · 2010
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Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
Goldstein, M., A. Dengel · 2012
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Predicting time-to-event from twitter messages
Tops, H., A. V. D. Bosch, F. Kunneman · 2013
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Sakurada, M., T. Yairi · 2014
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Time series contextual anomaly detection for detecting market manipulation in stock market
Golmohammadi, K., O. R. Zaiane · 2015
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Evaluating real-time anomaly detection algorithms – the numenta anomaly benchmark
Lavin, A., S. Ahmad · 2015
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Long short term memory networks for anomaly detection in time series
Malhotra, P., L. Vig, G. M. Shroff, et al · 2015
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Time series anomaly discovery with grammar-based compression
Senin, P., J. Lin, X. Wang, et al · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., P. Fischer, T. Brox · 2015
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Forecasting: Principles and Practice
Hyndman, R., G. Athanasopoulos · 2018
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Using machine learning methods to forecast if solar flares will be associated with cmes and seps
Inceoglu, F., J. H. Jeppesen, P. Kongstad, et al · 2018
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A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees
De Caigny, A., K. Coussement, K. W. De Bock · 2018
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Forecasting Heroin Overdose Occurrences from Crime Incidents
Ertugrul, A. M., Y.-R. Lin, C. Mair, et al · 2018
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Incomplete label multi-task ordinal regression for spatial event scale forecasting
Gao, Y., L. Zhao · 2018
Cited alongside, same era.
Forecasting natural events using axonal delay
Reid, D., A. Jaafar Hussain, H. Tawfik, et al · 2018
Cited alongside, same era.
Pairwise-ranking based collaborative recurrent neural networks for clinical event prediction
Copod: Copula-based outlier detection
Li, Z., Y. Zhao, N. Botta, et al · 2020
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Anomaly detection in time series
Borges, H., R. Akbarinia, F. Masseglia · 2021
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Anomaly transformer: Time series anomaly detection with association discrepancy
Xu, J., H. Wu, J. Wang, et al · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., S. Zhang, J. Peng, et al · 2021
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Event prediction in the big data era: A systematic survey
Zhao, L · 2021
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Exathlon: a benchmark for explainable anomaly detection over time series
Jacob, V., F. Song, A. Stiegler, et al · 2021
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Qiao, Z., S. Zhao, C. Xiao, et al · 2018
Cited alongside, same era.
Back to the beginning: Starting point detection for early recognition of ongoing human actions
Wang, B., M. Hoai · 2018
Cited alongside, same era.
A deep learning approach for anomaly detection based on sae and lstm in mechanical equipment
Li, Z., J. Li, Y. Wang, et al · 2019
Cited alongside, same era.
Time-series anomaly detection service at microsoft
Ren, H., B. Xu, Y. Wang, et al · 2019
Cited alongside, same era.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Su, Y., Y. Zhao, C. Niu, et al · 2019
Cited alongside, same era.
Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting , chap. ,, pages ,
Li, S., X. Jin, Y. Xuan, et al · 2019
Cited alongside, same era.
U-time: A fully convolutional network for time series segmentation applied to sleep staging
Perslev, M., M. Jensen, S. Darkner, et al · 2019
Cited alongside, same era.
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Tranad: deep transformer networks for anomaly detection in multivariate time series data
Tuli, S., G. Casale, N. R. Jennings · 2022
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress (extended abstract)
Wu, R., E. J. Keogh · 2022
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Anomaly detection in time series: a comprehensive evaluation
Schmidl, S., P. Wenig, T. Papenbrock · 2022
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Lead time analysis for uavs’ failure prediction in u-space
Asghari, O., N. Ivaki, H. Madeira · 2023
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Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes
Cerqueira, V., L. Torgo, C. Soares · 2023
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U-net inspired transformer architecture for far horizon time series forecasting
Madhusudhanan, K., J. Burchert, N. Duong-Trung, et al · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., N. H. Nguyen, P. Sinthong, et al · 2023
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Timeseriesbench: An industrial-grade benchmark for time series anomaly detection models
Si, H., C. Pei, H. Cui, et al · 2024
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Kernel-u-net: Multivariate time series forecasting using custom kernels
You, J., R. Natowicz, A. Cela, et al · 2024
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