Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress.IEEE Transactions on Knowledge and Data En- gineering, page 1 ˆa=C“1, 2021
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Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
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Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
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Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.