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 …
A space-embedding strategy for anomaly detection in multivariate time series
Anomaly detection of time series has always been a hot topic in academia and industry.
However, many existing multivariant time series methods suffer from common challenges …
However, many existing multivariant time series methods suffer from common challenges …
A novel deep learning approach for anomaly detection of time series data
Z Ji, J Gong, J Feng - Scientific Programming, 2021 - Wiley Online Library
Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …
occurrence of anomalies in time series may indicate that some faults or disease will occur …
A novel multivariate time-series anomaly detection approach using an unsupervised deep neural network
With the development of hardware technology, we can collect increasingly reliable time
series data, in which time series anomaly detection is an important task to find problems in …
series data, in which time series anomaly detection is an important task to find problems in …
Robust unsupervised anomaly detection with variational autoencoder in multivariate time series data
Accurate detection of anomalies in multivariate time series data has attracted much attention
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …
Time series anomaly detection using transformer-based gan with two-step masking
Time series anomaly detection is a task that determines whether an unseen signal is normal
or abnormal, and it is a crucial function in various real-world applications. Typical approach …
or abnormal, and it is a crucial function in various real-world applications. Typical approach …
Tunnel surface settlement forecasting with ensemble learning
Ground surface settlement forecasting in the process of tunnel construction is one of the
most important techniques towards sustainable city development and preventing serious …
most important techniques towards sustainable city development and preventing serious …
An Experimental Evaluation of Anomaly Detection in Time Series
A Zhang, S Deng, D Cui, Y Yuan, G Wang - Proceedings of the VLDB …, 2023 - dl.acm.org
Anomaly detection in time series data has been studied for decades in both statistics and
computer science. Various algorithms have been proposed for different scenarios, such as …
computer science. Various algorithms have been proposed for different scenarios, such as …
Anomaly detection paradigm for multivariate time series data mining for healthcare
Time series data are significant, and are derived from temporal data, which involve real
numbers representing values collected regularly over time. Time series have a great impact …
numbers representing values collected regularly over time. Time series have a great impact …