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Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic K-Means Clustering Algorithm for Outlier Detection
inferredcites →https://doi.org/10.1007/978-3-030-58920-2_14openalex-snapshot
Model-Based Error Detection for Industrial Automation Systems Using LSTM Networks
inferredcites →https://doi.org/10.1016/j.ces.2022.117467openalex-snapshot
Semi-supervised LSTM ladder autoencoder for chemical process fault diagnosis and localization
inferredcites →https://doi.org/10.1016/j.neucom.2019.07.034openalex-snapshot
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.004openalex-snapshot
Measuring the usefulness of hidden units in Boltzmann machines with mutual information
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inferredcites →https://doi.org/10.1061/(asce)0887-3801(2004)18:2(105)openalex-snapshot
Data Division for Developing Neural Networks Applied to Geotechnical Engineering
inferredcites →https://doi.org/10.1109/atit49449.2019.9030505openalex-snapshot
Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders
inferredcites →https://doi.org/10.1109/cvpr.2010.5539957openalex-snapshot
Deconvolutional networks
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Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot
Anomaly detection
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
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LOF
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Deep Learning for Anomaly Detection
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Online Forecasting and Anomaly Detection Based on the ARIMA Model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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