Pith Number

pith:GXIUZS4F

pith:2026:GXIUZS4F5TDJTSCVLQCLZNUCR6

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PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

Jinju Park, Seokho Kang

A patch-based CNN method for time-series anomaly detection surpasses complex models on benchmarks.

arxiv:2602.01359 v2 · 2026-02-01 · cs.LG · cs.AI

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.

C2weakest assumption

That the learned patch embeddings reliably separate normal from anomalous temporal patterns and that comparison to training-set normal patches produces a valid anomaly score without additional calibration or post-hoc tuning.

C3one line summary

PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.

References

37 extracted · 37 resolved · 1 Pith anchors

[1] doi: 10.1145/3394486.3403392. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.arXiv preprint arXiv:1803.0 · doi:10.1145/3394486.3403392

[2] Mvtec AD - A comprehensive real-world dataset for unsupervised anomaly detection 2019 · doi:10.1109/cvpr.2019.00982

[3] Paul Boniol, John Paparrizos, Themis Palpanas, and Michael J

[4] Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, and John Paparrizos · doi:10.14778/3467861.3467865

[5] Breunig, Hans-Peter Kriegel, Raymond T 2026

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:00.110304Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

35d14ccb85ecc699c8555c04bcb6828f84964abbad160f1fdc63f11fa5da0cf6

Aliases

arxiv: 2602.01359 · arxiv_version: 2602.01359v2 · doi: 10.48550/arxiv.2602.01359 · pith_short_12: GXIUZS4F5TDJ · pith_short_16: GXIUZS4F5TDJTSCV · pith_short_8: GXIUZS4F

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GXIUZS4F5TDJTSCVLQCLZNUCR6 \
  | 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: 35d14ccb85ecc699c8555c04bcb6828f84964abbad160f1fdc63f11fa5da0cf6

Canonical record JSON

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    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-01T17:52:59Z",
    "title_canon_sha256": "9f7972cca50d92f120d82a70c485e0e64b534720c727f14bb15b83ff3d7af82d"
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