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Examines how token transition rates influence anomaly detection methods in TSB-AD-U datasets, suggesting no advantages in performance.
Pre-registration of the predictions, frozen cutoffs, and analysis plan for testing whether the k-means token transition rate predicts when successor-field tokenization (SFVT) outperforms position-based tokenization on TSB-AD-U anomaly detection. Written before any anomaly-detection result was computed; no anomaly label was read in producing the accompanying atlas. Includes the atlas over 867 TSB-AD-U series and named datasets where we predict the method will show no advantage.
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Bunyamin Ergen (2026) studied this question.
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