VITS-AD Evaluation Suite

Companion artifact for the NeurIPS 2026 Evaluations & Datasets (E&D) Track submission VITS-AD: A Regime-Aware Evaluation Suite for Frozen-Vision Time-Series Anomaly Detection.

This dataset is not a new corpus. It bundles the evaluation outputs produced by the VITS-AD pipeline and the raw-space Mahalanobis baseline so that future work can:

1. Reproduce paper tables and statistical tests without re-running the full vision pipeline.
2. Run paired comparisons (Wilcoxon, paired-99 UCR) directly on the per-window scores.
3. Audit the regime classification (amplitude vs. structural) against the underlying evidence artifacts.

The submission is double-blind; this dataset card is anonymous. Source code is at https://github.com/evaldataset/VITS-AD (reviewer-routed via anonymous.4open.science).

Contents

Folder	Purpose	Size
regime_labels/	Per-dataset regime annotation + classifier features	<1 KB
ledgers/	JSON ledgers underlying main-paper claims	64 KB
ucr_canonical/	UCR 109/99 aggregated and per-series metrics	168 KB
multiseed_scores/	Per-window scores + labels for 5 seeds × {LP,RP} × {PSM,MSL,SMAP}, no model weights	9.8 MB
sample_renderings/	Pipeline diagram and regime-gain figures	1 MB

Total: ~11 MB.

What this is for (E&D Track scope)

The submission's contribution is benchmark analysis and evaluation methodology, not a new dataset. We therefore distribute:

• The regime axis (amplitude vs. structural) along which vision rendering does and does not pay off.
• The paired-99 UCR comparison that demonstrates Wilcoxon $p<10^{-7}$ in favour of the vision pipeline on the structural univariate regime.
• The per-seed scores that allow re-running paired Wilcoxon and bootstrap confidence intervals on the multiseed PSM/MSL/SMAP runs.
• The calibration and FPS ledgers that back the compute-disclosure and CalibGuard tables in the paper supplement.

We do not redistribute the raw benchmark datasets (SMD, PSM, MSL, SMAP, UCR Anomaly Archive). License and download paths for those upstream benchmarks are listed in the paper's Asset Credits table.

Files

regime_labels/regime_labels.json

Per-dataset regime label, channel count, raw vs. VITS-AD AUC-ROC, and the five classifier features. The accompanying notes record the in-sample classifier accuracy ($90.1%$), the majority-class baseline ($88.1%$), and the leave-one-dataset-out CV collapse to chance — i.e., the regime axis is descriptive, not a deployable predictor.

ledgers/

File	Backed claim
improved_ensemble_results.json	SMD 28-entity macro AUC-ROC for VITS-AD vs. raw Mahalanobis
multiseed_results.json	$n=5$ seed mean ± std for PSM/MSL/SMAP × {LP, RP}
multiseed_ensemble_summary.json	Rank-mean ensemble across renderers per dataset
optimized_ensemble.json	Oracle renderer-adaptive scoring
calibguard_multidataset.json	Realized FAR vs. target FAR (empirical diagnostic)
fps_benchmark.json	FPS, parameter count, and compute disclosure
clip_backbone_comparison.json	DINOv2 vs. CLIP backbone ablation
ucr_results.json	Legacy UCR aggregate (paired-99 in ucr_canonical/)
view_disagree_sweep.json	Cross-view disagreement scoring sweep

ucr_canonical/

Authoritative UCR ledgers used by every UCR claim in the paper:

File	Description
summary.json	109-series VITS-AD aggregate
paired_99.json	99-series paired comparison vs. raw Mahalanobis
combined_109.json	Per-series VITS-AD scores
per_series.json	Per-series metric breakdown
eligible_list.json	List of the 109 eligible UCR series
ucr_canonical.json	Combined manifest

multiseed_scores/

Layout: multiseed_scores/{psm,msl,smap}/{line_plot,recurrence_plot}/seed_{42,123,456,789,2024}/

Each leaf directory contains:

• scores.npy — per-window anomaly score (float64)
• labels.npy — per-window ground-truth label (int64, ${0,1}$)
• metrics.json — AUC-ROC, AUC-PR, best-F1, F1-PA for that seed

Model checkpoints (best_model.pt) are intentionally not redistributed to keep the bundle compact; they can be regenerated from the source repo.

sample_renderings/

PDFs of the pipeline diagram (Figure 1), per-dataset regime gain (Figure 3a), and the regime-map scatter (Figure 4 in the supplement).

Reproducing the paper's statistical tests

import json, numpy as np
from scipy.stats import wilcoxon

base = "multiseed_scores/psm/line_plot"
seeds = [42, 123, 456, 789, 2024]
aucs = []
for s in seeds:
    m = json.load(open(f"{base}/seed_{s}/metrics.json"))
    aucs.append(m["auc_roc"])
print(f"PSM-LP mean ± std: {np.mean(aucs):.4f} ± {np.std(aucs, ddof=1):.4f}")

# Paired-99 UCR Wilcoxon (vision vs. raw Mahalanobis)
paired = json.load(open("ucr_canonical/paired_99.json"))
stat, p = wilcoxon(paired["vits_ad_auc"], paired["raw_maha_auc"])
print(f"Paired-99 UCR Wilcoxon p={p:.2e}")

License

MIT. All redistributed JSON ledgers, regime annotations, and rendered example PDFs are original work of the (anonymous) authors and are released under MIT alongside the source repository.

Citation

@inproceedings{vitsad2026,
  title  = {{VITS-AD}: A Regime-Aware Evaluation Suite for Frozen-Vision Time-Series Anomaly Detection},
  author = {Anonymous},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
  year   = {2026}
}