Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection

arXiv:2610.20243v2 Announce Type: replace-cross Abstract: 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. Therefore, a practical and scalable solution is to develop a model-selection method that, for a given time series, selects the anomaly detector most likely to perform well. Nevertheless, the model

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