A review on big data based on deep neural network approaches
Big data analytics has become a significant trend for many businesses as a result of the
daily acquisition of enormous volumes of data. This information has been gathered because …
daily acquisition of enormous volumes of data. This information has been gathered because …
[HTML][HTML] Unsupervised anomaly detection for IoT-based multivariate time series: Existing solutions, performance analysis and future directions
The recent wave of digitalization is characterized by the widespread deployment of sensors
in many different environments, eg, multi-sensor systems represent a critical enabling …
in many different environments, eg, multi-sensor systems represent a critical enabling …
Transformers in time series: A survey
Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …
processing and computer vision, which also triggered great interest in the time series …
Deep isolation forest for anomaly detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …
in recent years due to its general effectiveness across different benchmarks and strong …
Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach
Y Song, R Xin, P Chen, R Zhang, J Chen… - Future Generation …, 2023 - Elsevier
Cloud computing provides scalable and elastic resources to customers as a low-cost, on-
demand utility service. Multivariate time series anomaly detection is crucial to promise the …
demand utility service. Multivariate time series anomaly detection is crucial to promise the …
TFAD: A decomposition time series anomaly detection architecture with time-frequency analysis
Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …
dependencies and the limited label data. Although some algorithms including both …
Deep learning for time series anomaly detection: A survey
Time series anomaly detection has applications in a wide range of research fields and
applications, including manufacturing and healthcare. The presence of anomalies can …
applications, including manufacturing and healthcare. The presence of anomalies can …
Contrastive autoencoder for anomaly detection in multivariate time series
H Zhou, K Yu, X Zhang, G Wu, A Yazidi - Information Sciences, 2022 - Elsevier
With the proliferation of the Internet of Things, a large amount of multivariate time series
(MTS) data is being produced daily by industrial systems, corresponding in many cases to …
(MTS) data is being produced daily by industrial systems, corresponding in many cases to …
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
M Ma, L Han, C Zhou - Advanced Engineering Informatics, 2023 - Elsevier
In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …
be performed efficiently and accurately, it will bring great security risks to industrial systems …
Weakly supervised anomaly detection: A survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such
as detecting emerging diseases, identifying financial frauds, and detecting fake news …
as detecting emerging diseases, identifying financial frauds, and detecting fake news …