---
license: mit
language:
- en
pretty_name: VITS-AD Evaluation Suite
size_categories:
- 10K<n<100K
task_categories:
- time-series-forecasting
tags:
- anomaly-detection
- time-series
- evaluation
- benchmark
- frozen-vision
- regime-analysis
- negative-results
- neurips-2026
---

# 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

```python
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

```bibtex
@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}
}
```