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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Overlapping inference windows improve reconstruction-based time series anomaly detection by up to 28% relative gain across models on TSB-AD and UCR benchmarks and can alter rankings.
TPA-AD generates boundary-near pseudo-anomalies via reconstruction, applies contrastive learning, and uses KNN to score anomalies in bearing time series with only normal training samples.
citing papers explorer
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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.
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Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection
Overlapping inference windows improve reconstruction-based time series anomaly detection by up to 28% relative gain across models on TSB-AD and UCR benchmarks and can alter rankings.
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TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection
TPA-AD generates boundary-near pseudo-anomalies via reconstruction, applies contrastive learning, and uses KNN to score anomalies in bearing time series with only normal training samples.