Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods

N Mejri, L Lopez-Fuentes, K Roy, P Chernakov… - Expert Systems with …, 2024 - Elsevier
Unsupervised anomaly detection in time-series has been extensively investigated in the
literature. Notwithstanding the relevance of this topic in numerous application fields, a …

Dive into time-series anomaly detection: A decade review

P Boniol, Q Liu, M Huang, T Palpanas… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advances in data collection technology, accompanied by the ever-rising volume and
velocity of streaming data, underscore the vital need for time series analytics. In this regard …

Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection

Y Chen, C Zhang, M Ma, Y Liu, R Ding, B Li… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …

Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

F Rewicki, J Denzler, J Niebling - Applied Sciences, 2023 - mdpi.com
Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …

ADTCD: An Adaptive Anomaly Detection Approach Toward Concept Drift in IoT

L Xu, X Ding, H Peng, D Zhao… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The data collected by sensors is streaming data in the Internet of Things (IoT). Although
existing deep-learning-based anomaly detection methods generally perform well on static …

[HTML][HTML] Identifying, exploring, and interpreting time series shapes in multivariate time intervals

G Shirato, N Andrienko, G Andrienko - Visual informatics, 2023 - Elsevier
We introduce a concept of episode referring to a time interval in the development of a
dynamic phenomenon that is characterized by multiple time-variant attributes. A data …

OneShotSTL: one-shot seasonal-trend decomposition for online time series anomaly detection and forecasting

X He, Y Li, J Tan, B Wu, F Li - arXiv preprint arXiv:2304.01506, 2023 - arxiv.org
Seasonal-trend decomposition is one of the most fundamental concepts in time series
analysis that supports various downstream tasks, including time series anomaly detection …

Deep contrastive one-class time series anomaly detection

R Wang, C Liu, X Mou, K Gao, X Guo, P Liu, T Wo… - Proceedings of the 2023 …, 2023 - SIAM
The accumulation of time-series data and the absence of labels make time-series Anomaly
Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based …

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 …

An efficient content-based time series retrieval system

CCM Yeh, H Chen, X Dai, Y Zheng, J Wang… - Proceedings of the …, 2023 - dl.acm.org
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for
users to interact with time series emerged from multiple domains, such as finance …