Outlier detection for temporal data: A survey
In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …
decades. Recently, with advances in hardware and software technology, there has been a …
An attention-based ConvLSTM autoencoder with dynamic thresholding for unsupervised anomaly detection in multivariate time series
As a substantial amount of multivariate time series data is being produced by the complex
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
Unsupervised anomaly detection in multivariate spatio-temporal data using deep learning: early detection of COVID-19 outbreak in Italy
Y Karadayi, MN Aydin, AS Öǧrencí - Ieee Access, 2020 - ieeexplore.ieee.org
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide
variety of applications such as earth science, traffic monitoring, fraud and disease outbreak …
variety of applications such as earth science, traffic monitoring, fraud and disease outbreak …
Monitor: An abnormality detection approach in buildings energy consumption
H Rashid, P Singh - 2018 IEEE 4th international conference on …, 2018 - ieeexplore.ieee.org
With the growth of smart cities, more buildings are now being instrumented with smart
meters for providing better energy efficiency for sustainable development. Buildings …
meters for providing better energy efficiency for sustainable development. Buildings …
A hybrid deep learning framework for unsupervised anomaly detection in multivariate spatio-temporal data
Y Karadayı, MN Aydin, AS Öğrenci - Applied Sciences, 2020 - mdpi.com
Multivariate time-series data with a contextual spatial attribute have extensive use for finding
anomalous patterns in a wide variety of application domains such as earth science …
anomalous patterns in a wide variety of application domains such as earth science …
Multi-user energy consumption monitoring and anomaly detection with partial context information
Anomaly detection is an important problem in building energy management in order to
identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies …
identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies …
Analysis of the virtual enterprise using distributed supply chain modeling and simulation: an application of e-SCOR
MW Barnett, CJ Miller - 2000 winter simulation conference …, 2000 - ieeexplore.ieee.org
Supply chains are large systems consisting of many entities interacting in complex ways.
The challenge faced by companies is how to design and manage such systems. Modeling …
The challenge faced by companies is how to design and manage such systems. Modeling …
[PDF][PDF] Real time contextual collective anomaly detection over multiple data streams
Anomaly detection has always been a critical and challenging problem in many application
areas such as industry, healthcare, environment and finance. This problem becomes more …
areas such as industry, healthcare, environment and finance. This problem becomes more …
Adaptive real‐time anomaly detection in cloud infrastructures
Cloud computing has become increasingly popular, which has led many individuals and
organizations towards cloud storage systems. This move is motivated by benefits such as …
organizations towards cloud storage systems. This move is motivated by benefits such as …
Adaptive threshold for anomaly detection using time series segmentation
Time series data are generated from almost every domain and anomaly detection becomes
extremely important in the last decade. It consists in detecting anomalous patterns through …
extremely important in the last decade. It consists in detecting anomalous patterns through …