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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
RSD fits shared three-anchor charts S_t to GPT-2 hidden states for target words, derives co-membership readouts M_t, and audits against WiC same-sense labels, passing 16 of 53 words as diagnostic coverage.
citing papers explorer
-
Fast and Accurate Anomaly Detection in Time Series
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.
-
RSD: Moving Local Triangular Charts for Auditing Language-Model Hidden States
RSD fits shared three-anchor charts S_t to GPT-2 hidden states for target words, derives co-membership readouts M_t, and audits against WiC same-sense labels, passing 16 of 53 words as diagnostic coverage.