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ARXIV:2604.02445 · TIME-SERIES ANOMALY DETECTION · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02445TIME-SERIES ANOMALY DETECTIONSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNChin-Chia Michael Yeh · arXiv
An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection.
Opportunity summary
Pain An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that…
Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and…
Time-Series Anomaly Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection.
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Paper Pack
10.48550/arXiv.2604.02445An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection.
Abstract
Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre-sorted multidimensional aggregation, efficient exclusion-zone-aware k-nearest-neighbor (kNN) retrieval for repeated anomalies, and moving-average post-processing. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset characteristics.The open-source implementation is available at https://github.com/mcyeh/mmpad_tsb.
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Dimensions overall score 7.0
PROBLEM
An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univ...
METHOD
Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-s...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corres...
WHY NOW
Time-Series Anomaly Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Time-Series Anomaly Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An open-source benchmark and implementation of Matrix Profile methods for scalable and interpretable time-series anomaly detection.
Segment
Time-Series Anomaly Detection
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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reason
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proof status
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confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Build readiness
BuildPassport EvidenceState
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unknown
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
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Buyer clarity
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Defensibility
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
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Gaps
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Classify regulatory flags before commercialization planning.
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People
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Prototype owner missing.
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People
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Regulatory need unclassified.
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ARTIFACTS
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