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.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network
12 Pith papers cite this work. Polarity classification is still indexing.
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POST uses prior-observation adversarial learning on adjacency matrices to reduce spatial over-generalization in graph-based multivariate time series anomaly detection and achieves new SOTA results on detection and channel-wise localization.
A novel unsupervised anomaly detection method for time series using Haar wavelets and a designed t-test outperforms state-of-the-art benchmarks across 343 datasets.
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
D-HTM adds a shared associative memory to hierarchical temporal memory so that precursor signatures learned on one entity can trigger preemptive warnings on related entities, yielding an average 8.1-sample lead time on tested datasets.
Latent SDE generative model for anomaly detection in sparse irregular multivariate time series outperforms baselines on six benchmarks and stays robust under severe sparsity.
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
Introduces a cyclic-dynamics dataset for industrial MTSAD and benchmarks federated anomaly detection methods on it and a public dataset.
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
PaAno+ extends the original PaAno with multiscale feature extraction, cross-variable fusion attention, and a temporal patch sorting pretext task to report state-of-the-art results on the TSB-AD benchmark for univariate and multivariate anomaly detection.
Temporal convolutional autoencoders outperform isolation forests and other autoencoder variants for unsupervised anomaly detection on a real-world industrial dataset with non-periodic multi-scale dynamics.