Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …
enabling a large amount of data to be gathered over time and thus generating time series …
LRR-Net: An interpretable deep unfolding network for hyperspectral anomaly detection
Considerable endeavors have been expended toward enhancing the representation
performance for hyperspectral anomaly detection (HAD) through physical model-based …
performance for hyperspectral anomaly detection (HAD) through physical model-based …
Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder
With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
[HTML][HTML] Co-evolution of platform architecture, platform services, and platform governance: Expanding the platform value of industrial digital platforms
Industrial manufacturers increasingly develop digital platforms in the business-to-business
(B2B) context. This emergent form of digital platforms requires a profound yet little …
(B2B) context. This emergent form of digital platforms requires a profound yet little …
Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
Survey on categorical data for neural networks
JT Hancock, TM Khoshgoftaar - Journal of big data, 2020 - Springer
This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …
to neural networks. Techniques for using qualitative data in neural networks are well known …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
A tutorial review of neural network modeling approaches for model predictive control
An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …
presented along with its use in model predictive control (MPC). A tutorial on the construction …
[PDF][PDF] Outlier detection for time series with recurrent autoencoder ensembles.
We propose two solutions to outlier detection in time series based on recurrent autoencoder
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …