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.
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
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.
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.
CRAFTIIF uses 500 random analytic wavelet features across four families and five structured isolation forests to target four anomaly types, achieving first place on mTSBench VUS-PR at 0.463.
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.
citing papers explorer
-
POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
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.