Feb 17, 2024 · In response to the four challenges mentioned earlier, we propose TimeSeriesBench, an industrial-grade benchmark for evaluating time series anomaly detection.

Feb 16, 2024 · We assess the performance of existing algorithms across more than 168 evaluation settings and provide comprehensive analysis for the future design of anomaly ...

Jun 13, 2024 · TSB-AD features high-quality labeled time series from a variety of domains, characterized by high variability in length and types of anomalies.

Nov 19, 2023 · In this paper, we advance the benchmarking of multivariate time series anomaly detection from datasets, evaluation metrics, and algorithm comparison.

Jun 11, 2024 · We propose a novel framework called FastUTS-AD that achieves improved data efficiency and reduced computational overhead compared to existing UTS-AD models ...

Jul 16, 2024 · Exathlon: a benchmark for explainable anomaly detection over time series. Proceedings of the VLDB Endowment (PVLDB) Journal, 2021. [11] Eamonn J. Keogh ...

Jul 9, 2024 · TSB-UAD provides a valuable, reproducible, and frequently updated resource to establish a leaderboard of univariate time-series anomaly detection methods. PVLDB ...

Jan 29, 2024 · One suite focuses only on fully synthetic sequences for testing algorithms with complete knowledge of the sequences.

Nov 21, 2023 · Detecting anomalous subsequences in time series data is an important task in areas ranging from manufacturing processes over finance applications to health care ...

Nov 18, 2023 · This paper provides a comprehensive overview of the metrics used for the evaluation of time series anomaly detection methods, and also defines a taxonomy of ...