github Not archived

linkedin/luminol

Anomaly Detection and Correlation library

1 awesome list

Quick read

Stars
1,231
Forks
217
Open issues
35
Commits
72

Activity and growth

Latest capture 2026-10-09 03:09

Stars · last 7 days
No history
Commits · last 7 days
No history
Stars since tracking
-1
Stored snapshots
9

Classification

Technology stack

Metadata

Language
Python
License
Apache-2.0
Default branch
master
Created
2015-11-18
First commit
2015-11-18
Last pushed
2025-08-22
GitHub updated
2026-09-18
Last synced
2026-10-09 03:09
Stack scanned
2026-10-09 03:09
Archived
No

Archive status reflects the last catalog sync, not a maintenance assessment. Check recent releases, issue responses, and fit for your project with the repository shortlisting checklist.

AI development signals

0 paths

Agent instructions and tool configuration found in this repository.

No config files detected.

Growth history

Tracked growth

9 observed captures since 2026-05-22. Observed captures are shown by default.

Stars from first capture -1

Chart data

Observed captures only

Time horizon

All tracked data

Custom date range

Stars history

Observed snapshots

Commits history

Observed snapshots

Similar repositories

Nearest indexed repositories by embedding similarity.

thuml/Time-Series-Library

A Library for Advanced Deep Time Series Models for General Time Series Analysis.

12,842 stars
Python 1 awesome list

AICoE/log-anomaly-detector

Log Anomaly Detection - Machine learning to detect abnormal events logs

344 stars
Jupyter Notebook 1 awesome list

lmnr-ai/lmnr

Laminar - open-source observability platform purpose-built for AI agents. YC S24.

3,388 stars
TypeScript 2 awesome lists

functime-org/functime

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

1,185 stars
Python 1 awesome list

tslearn-team/tslearn

The machine learning toolkit for time series analysis in Python

3,184 stars
Python 1 awesome list

christophmark/bayesloop

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

172 stars
Python 1 awesome list