# yzhao062/anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!

Repository: https://github.com/yzhao062/anomaly-detection-resources
Canonical: https://ross.abutalabs.com/products/anomaly-detection-resources
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: outlier-detection, anomaly-detection, outlier, outlier-ensembles, time-series-analysis, data-mining, awesome, awesome-list, unsupervised-learning, fraud, fraud-detection, machine-learning, graph-neural-networks, large-language-models, llm, vlm, vlms
Last push: 2026-03-02T04:42:20+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 70, release rhythm 35, longevity 100
- inputs: {"age_days": 3031, "days_push": 184, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9370, forks 1804 (observed 2026-08-28T04:10:30.889347+00:00)

## What it is
A curated awesome-list of anomaly detection (outlier detection) learning resources including books, papers, courses, videos, datasets, and software toolboxes. It is actively updated and now covers LLM/VLM-based anomaly detection research.

## Use cases
- find papers on anomaly detection
- learn outlier detection from scratch
- find open-source anomaly detection libraries
- find datasets for outlier detection benchmarking
- research LLM-based anomaly detection
- find time series anomaly detection resources
- find fraud detection learning materials

## When to choose
- you need a starting point to learn anomaly detection
- you want a curated index of papers, books, and toolboxes in one place
- you are surveying the field including recent LLM/VLM approaches

## When to avoid
- you need a working anomaly detection library rather than a resource list
- you need executable code or benchmarks rather than references

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, developer-tools
- domain: machine-learning, data-science, awesome-lists, artificial-intelligence, large-language-models
- platform: python, cross-platform
- tags: awesome-list, anomaly-detection, outlier-detection, fraud-detection, time-series, papers, datasets, curated-resources

## Member repositories
- yzhao062/anomaly-detection-resources (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:30.889347+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:22:25.138093+00:00, confidence not recorded.
  - readme: https://github.com/yzhao062/anomaly-detection-resources (fetched 2026-08-28T04:10:30.889347+00:00, sha 4678a75b0382)
- Data as of 2026-08-30T08:39:29.467469+00:00.