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 …
SAND: streaming subsequence anomaly detection
With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …
methods need to operate over streams of values and handle drifts in data distribution …
Series2graph: Graph-based subsequence anomaly detection for time series
P Boniol, T Palpanas - arXiv preprint arXiv:2207.12208, 2022 - arxiv.org
Subsequence anomaly detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches proposed so far in the …
applications in a wide range of domains. However, the approaches proposed so far in the …
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 …
Return of the lernaean hydra: Experimental evaluation of data series approximate similarity search
Data series are a special type of multidimensional data present in numerous domains,
where similarity search is a key operation that has been extensively studied in the data …
where similarity search is a key operation that has been extensively studied in the data …
Unsupervised and scalable subsequence anomaly detection in large data series
Subsequence anomaly (or outlier) detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches that have been …
applications in a wide range of domains. However, the approaches that have been …
Matrix profile goes MAD: variable-length motif and discord discovery in data series
In the last 15 years, data series motif and discord discovery have emerged as two useful and
well-used primitives for data series mining, with applications to many domains, including …
well-used primitives for data series mining, with applications to many domains, including …
Automated anomaly detection in large sequences
Subsequence anomaly (or outlier) detection in long sequences is an important problem with
applications in a wide range of domains. However, current approaches have severe …
applications in a wide range of domains. However, current approaches have severe …
Towards observability data management at scale
Observability has been gaining importance as a key capability in today's large-scale
software systems and services. Motivated by current experience in industry exemplified by …
software systems and services. Motivated by current experience in industry exemplified by …