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FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

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What this work says

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

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Ranking Outliers Using Symmetric Neighborhood Relationship
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Enhancing Effectiveness of Outlier Detections for Low Density Patterns
inferredcites →https://doi.org/10.1007/978-1-4614-6396-2_1openalex-snapshot
An Introduction to Outlier Analysis
inferredcites →https://doi.org/10.1007/978-3-319-08010-9_33openalex-snapshot
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-93034-3_46openalex-snapshot
DeepAD: A Generic Framework Based on Deep Learning for Time Series Anomaly Detection
inferredcites →https://doi.org/10.1007/978-94-015-3994-4openalex-snapshot
Identification of Outliers
inferredcites →https://doi.org/10.1007/s10994-016-5567-7openalex-snapshot
Expected similarity estimation for large-scale batch and streaming anomaly detection
inferredcites →https://doi.org/10.1016/0895-4356(90)90005-aopenalex-snapshot
The individual over time: Time series applications in health care research
inferredcites →https://doi.org/10.1016/j.cviu.2018.02.006openalex-snapshot
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
inferredcites →https://doi.org/10.1016/j.neucom.2017.04.070openalex-snapshot
Unsupervised real-time anomaly detection for streaming data
inferredcites →https://doi.org/10.1016/j.scs.2017.07.016openalex-snapshot
Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings
inferredcites →https://doi.org/10.1109/access.2017.2686092openalex-snapshot
Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond
inferredcites →https://doi.org/10.1109/access.2017.2747560openalex-snapshot
Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things
inferredcites →https://doi.org/10.1109/access.2018.2886457openalex-snapshot
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
inferredcites →https://doi.org/10.1109/cca.2015.7320737openalex-snapshot
A data-driven approach for fault diagnosis in HVAC chiller systems
inferredcites →https://doi.org/10.1109/dsaa.2015.7344872openalex-snapshot
Anomaly detection in ECG time signals via deep long short-term memory networks
inferredcites →https://doi.org/10.1109/iccsn.2010.55openalex-snapshot
ARIMA Based Network Anomaly Detection
inferredcites →https://doi.org/10.1109/icdm.2008.17openalex-snapshot
Isolation Forest
inferredcites →https://doi.org/10.1109/icmla.2015.141openalex-snapshot
Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark
inferredcites →https://doi.org/10.1109/ijcnn.2003.1223670openalex-snapshot
Time-series novelty detection using one-class support vector machines
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966038openalex-snapshot
Demystifying Numenta anomaly benchmark
inferredcites →https://doi.org/10.1109/inm.2011.5990537openalex-snapshot
Statistical techniques for online anomaly detection in data centers
inferredcites →https://doi.org/10.1109/mper.2002.4312577openalex-snapshot
ARIMA Models to Predict Next-Day Electricity Prices
inferredcites →https://doi.org/10.1109/tpwrs.2002.804943openalex-snapshot
ARIMA models to predict next-day electricity prices
inferredcites →https://doi.org/10.1109/tpwrs.2005.846054openalex-snapshot
Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models
inferredcites →https://doi.org/10.1109/wacv.2017.111openalex-snapshot
Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection
inferredcites →https://doi.org/10.1142/10655openalex-snapshot
Data Mining in Time Series and Streaming Databases
inferredcites →https://doi.org/10.1142/9789813228047_0007openalex-snapshot
Data Analytics: Industrial Perspective & Solutions for Streaming Data
inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot
Anomaly detection
inferredcites →https://doi.org/10.1145/2046707.2046741openalex-snapshot
MIDeA
inferredcites →https://doi.org/10.1145/2783258.2788611openalex-snapshot
Generic and Scalable Framework for Automated Time-series Anomaly Detection
inferredcites →https://doi.org/10.1145/342009.335388openalex-snapshot
LOF
inferredcites →https://doi.org/10.1145/342009.335437openalex-snapshot
Efficient algorithms for mining outliers from large data sets
inferredcites →https://doi.org/10.1155/2016/9653230openalex-snapshot
An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks
inferredcites →https://doi.org/10.1371/journal.pone.0155781openalex-snapshot
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security
inferredcites →https://doi.org/10.18637/jss.v027.i03openalex-snapshot
Automatic Time Series Forecasting: The forecast Package for R
inferredcites →https://doi.org/10.2174/2210327908666180731103713openalex-snapshot
Deep Learning Trends for Video Based Activity Recognition: A Survey
inferredcites →https://doi.org/10.26180/5090230openalex-snapshot
Automatic time series forecasting: the forecast package for R
inferredcites →https://doi.org/10.48550/arxiv.0710.3742openalex-snapshot
Bayesian Online Changepoint Detection
inferredcites →https://doi.org/10.48550/arxiv.1901.03407openalex-snapshot
Deep Learning for Anomaly Detection: A Survey
inferredcites →https://doi.org/10.5430/jms.v7n1p21openalex-snapshot
The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries

What points at this work

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

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Unsupervised anomaly detection in time-series
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