Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
In this paper, we tackle the aforementioned limitation and propose RAMSAD, a retrieval-based framework for model selection in time-series anomaly detection.
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Key points
- Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications.
- Despite the large number of anomaly detection methods proposed in the literature, recent benchmark studies have shown that no single detector performs best across highly heterogeneous time series.
- Given a new time series, our method queries a knowledge base of previously observed instances, retrieves the top-k most similar series, and transfers detector recommendations from their performance profiles.
- Overall, we demonstrate that similarity-based retrieval constitutes a strong and efficient foundation for model selection in time-series anomaly detection.
Sources (1)
- [1]Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:18 PM
In this paper, we tackle the aforementioned limitation and propose RAMSAD, a retrieval-based framework for model selection in time-series anomaly detection.
Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications.
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