zamanzadeh/CARLA
CARLA: A self-supervised contrastive learning model for time series anomaly detection. Enhances anomaly detection by learning robust representations of time series data.
This project helps operations engineers and data analysts identify unusual patterns in critical system telemetry. By feeding in continuous sensor readings or system logs, it can flag anomalies that might indicate equipment malfunction, security breaches, or performance degradation. The output helps users quickly pinpoint segments of time series data that deviate from normal behavior, allowing for proactive investigation and maintenance.
143 stars. No commits in the last 6 months.
Use this if you need to reliably detect unexpected events or performance issues within large volumes of unlabeled time series data, such as server metrics, sensor readings, or network traffic.
Not ideal if your time series data is already well-labeled with known anomaly types, as this tool is designed for scenarios where explicit anomaly labels are scarce.
Stars
143
Forks
19
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 18, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zamanzadeh/CARLA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.