Pith Number

pith:ZANPQFQE

pith:2025:ZANPQFQENNY4XCSUNJ5LNDFLCJ

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A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection

Jiarong Liu, Kaixiang Yang, Shuanghua Yang, Yujue Zhou, Yupeng Song

A problem-oriented taxonomy of time series anomaly detection metrics finds that most separate real detections from noise but NAB and Point-Adjust inflate easily under random scores.

arxiv:2511.18739 v2 · 2025-11-24 · cs.AI · cs.LG · stat.ML

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Claims

C1strongest claim

The results show that while most event-level metrics exhibit strong separability, several widely used metrics (e.g., NAB, Point-Adjust) demonstrate limited resistance to random-score inflation.

C2weakest assumption

That the six proposed dimensions capture the main evaluation challenges and that the genuine/random/oracle experimental scenarios sufficiently represent real application behavior without additional confounding factors.

C3one line summary

A problem-oriented taxonomy groups anomaly detection metrics into six dimensions and experiments show that some popular ones like NAB and Point-Adjust fail to resist random-score inflation.

References

51 extracted · 51 resolved · 0 Pith anchors

[1] Anomaly detection for iot time- series data: A survey, 2019

[2] Iot platforms: enabling the internet of things, 2016

[3] Idc forecasts connected iot devices to generate 79.4 zb of data in 2025, 2025

[4] Time series anomaly detection for cyber-physical systems via neural system identification and bayesian filtering, 2021

[5] Mac: Measuring the impacts of anomalies on travel time of multiple transportation systems, 2019

Receipt and verification

First computed	2026-05-17T23:39:17.052381Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c81af816046b71cb8a546a7ab68cab127609eed2841d90f24821100c030bae1a

Aliases

arxiv: 2511.18739 · arxiv_version: 2511.18739v2 · doi: 10.48550/arxiv.2511.18739 · pith_short_12: ZANPQFQENNY4 · pith_short_16: ZANPQFQENNY4XCSU · pith_short_8: ZANPQFQE

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZANPQFQENNY4XCSUNJ5LNDFLCJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c81af816046b71cb8a546a7ab68cab127609eed2841d90f24821100c030bae1a

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "1777b9c95f1f97ed4c527c589d7e475f02b540b453f530eadb29bf856351e30b",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.ML"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-11-24T04:09:04Z",
    "title_canon_sha256": "9b11092db4fec8952fa64938ae3a40ec6a48d8c79f11e1be65bb2b85918716db"
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    "kind": "arxiv",
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