Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all domains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experimenting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every available anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series characteristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods.
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Anomaly Detection with Multiple Reference Datasets in High Energy Physics
An important class of techniques for resonant anomaly detection in high energy
physics builds models that can distinguish between reference and target datasets,
where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWOLA) and Simulation Assisted Likelihood-free Anomaly
Detection (SALAD) rely on a single reference dataset. They cannot take advantage
of commonly-available multiple datasets and thus cannot fully exploit available
information. In this work, we propose generalizations of CWOLA and SALAD for
settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings
with real and synthetic data. As an added benefit, our generalizations enable us to
provide finite-sample guarantees, improving on existing asymptotic analyses.
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- Award ID(s):
- 2106707
- PAR ID:
- 10427110
- Date Published:
- Journal Name:
- NeurIPS Workshop on Machine Learning and the Physical Sciences (ML4PS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
