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Anomaly detection in time-series has a wide range of practical applications.
Maximum likelihood estimation of observer error-rates using the em algorithm
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A theoretical analysis of ndcg type ranking measures
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Generic and scalable framework for automated time-series anomaly detection
Nikolay Laptev, Saeed Amizadeh, and Ian Flint · 2015
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Long short term memory networks for anomaly detection in time series
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal, et al · 2015
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Efficient and robust automated machine learning
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How much does it cost to identify a critically ill child experiencing electrographic seizures?
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How to evaluate the quality of unsupervised anomaly detection algorithms?
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Data programming: Creating large training sets, quickly
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Robust random cut forest based anomaly detection on streams
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Model selection in contextual stochastic bandit problems
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A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A Lozano · 2021
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn Keogh · 2021
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Neural contextual anomaly detection for time series
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Online false discovery rate control for anomaly detection in time series
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Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications
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A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder
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Forecasting: principles and practice
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
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Unsupervised model selection for variational disentangled representation learning
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Weak supervision for affordable modeling of electrocardiogram data
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Automatic unsupervised outlier model selection
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Self-supervised learning for fast and scalable time series hyper-parameter tuning
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Novel meta-features for automated machine learning model selection in anomaly detection
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Anomaly detection in time series: a comprehensive evaluation
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