Anomaly detection in time series: a comprehensive evaluation
S Schmidl, P Wenig, T Papenbrock - Proceedings of the VLDB …, 2022 - dl.acm.org
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
TSB-UAD: an end-to-end benchmark suite for univariate time-series anomaly detection
The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …
Volume under the surface: a new accuracy evaluation measure for time-series anomaly detection
Anomaly detection (AD) is a fundamental task for time-series analytics with important
implications for the downstream performance of many applications. In contrast to other …
implications for the downstream performance of many applications. In contrast to other …
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 …
A survey on outlier explanations
E Panjei, L Gruenwald, E Leal, C Nguyen, S Silvia - The VLDB Journal, 2022 - Springer
While many techniques for outlier detection have been proposed in the literature, the
interpretation of detected outliers is often left to users. As a result, it is difficult for users to …
interpretation of detected outliers is often left to users. As a result, it is difficult for users to …
Choose wisely: An extensive evaluation of model selection for anomaly detection in time series
Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …
for the downstream performance of many applications. Despite increasing academic interest …
Matrix profile XXIV: scaling time series anomaly detection to trillions of datapoints and ultra-fast arriving data streams
Time series anomaly detection remains one of the most active areas of research in data
mining. In spite of the dozens of creative solutions proposed for this problem, recent …
mining. In spite of the dozens of creative solutions proposed for this problem, recent …
Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection
Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …
the efficient operation of large-scale systems across diverse domains. However, accurately …
Calibrated one-class classification for unsupervised time series anomaly detection
Time series anomaly detection is instrumental in maintaining system availability in various
domains. Current work in this research line mainly focuses on learning data normality …
domains. Current work in this research line mainly focuses on learning data normality …