Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
A Review on Outlier/Anomaly Detection in Time Series Data , year =
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
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.
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.
The authors create VisAnomBench with VLM-generated anomaly explanations and fine-tune VisAnomReasoner, reporting precision and F1 gains of at least 21 and 23 points on the new benchmark plus cross-benchmark improvements.
citing papers explorer
-
Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
-
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.
-
CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection
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.
-
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.