zamanzadeh/ts-anomaly-benchmark
Time-Series Anomaly Detection Comprehensive Benchmark
This project helps researchers and practitioners evaluate and compare different deep learning methods for detecting unusual patterns in time series data. It takes raw time series datasets, such as sensor readings or financial logs, and allows you to test various anomaly detection models to see which ones perform best. It's designed for data scientists, machine learning engineers, and domain experts who need to identify anomalies in their time-dependent data.
257 stars. No commits in the last 6 months.
Use this if you need to benchmark and select the most effective deep learning model for anomaly detection across various time-series datasets.
Not ideal if you're looking for a simple, out-of-the-box solution to apply anomaly detection without deep technical evaluation or if your data is not in a time-series format.
Stars
257
Forks
28
Language
—
License
MIT
Category
Last pushed
Sep 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zamanzadeh/ts-anomaly-benchmark"
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.