| Name | Version | Summary | date |
| fedot-ind |
0.4.1.2 |
Automated machine learning framework for time series analysis |
2024-03-07 12:51:52 |
| pyod |
1.1.3 |
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection) |
2024-02-09 06:50:32 |
| suod |
0.1.3 |
A Scalable Framework for Unsupervised Outlier Detection (Anomaly Detection) |
2024-02-08 01:53:44 |
| pygod |
1.1.0 |
A Python Library for Graph Outlier Detection (Anomaly Detection) |
2024-02-04 21:25:17 |
| pythresh |
0.3.6 |
A Python Toolbox for Outlier Detection Thresholding |
2024-02-03 11:33:45 |
| jumpavg |
0.4.2 |
Library for locating changes in time series by grouping results. |
2024-01-24 09:06:51 |
| OeSNN-AD |
1.0.1 |
OeSNN-UAD anomaly detector implementation for Python. |
2023-12-30 10:45:41 |
| anomalytics |
0.2.2 |
The ultimate anomaly detection library. |
2023-12-21 10:34:33 |
| eventdetector-ts |
1.1.0 |
EventDetector introduces a universal event detection method for multivariate time series. Unlike traditional deep-learning methods, it's regression-based, requiring only reference events. The robust stacked ensemble, from Feed-Forward Neural Networks to Transformers, ensures accuracy by mitigating biases. The package supports practical implementation, excelling in detecting events with precision, validated across diverse domains. |
2023-11-28 15:31:31 |
| xiezhi-ai |
0.0.0 |
Anomaly detection for one-dimensional data |
2023-10-21 21:28:27 |
| wzl |
0.0.1 |
Anomaly detection for one-dimensional data. |
2023-09-10 20:47:12 |
| odad |
0.0.1 |
Anomaly detection for one-dimensional data |
2023-09-10 20:21:56 |
| xiezhi-detect |
0.0.1 |
Anomaly detection for one-dimensional data |
2023-09-10 20:15:10 |
| xiezhi |
0.0.1 |
Anomaly detection for one-dimensional data |
2023-09-10 20:04:20 |
| deepod |
0.4.1 |
|
2023-09-06 09:19:13 |
| vae_anomaly_detection |
2.0.1 |
Pytorch/TF1 implementation of Variational AutoEncoder for anomaly detection following the paper "Variational Autoencoder based Anomaly Detection using Reconstruction Probability by Jinwon An, Sungzoon Cho" |
2023-08-28 19:41:39 |
| trendalation |
1.0.2 |
|
2023-08-12 21:23:39 |
| adbench |
0.1.11 |
Python package of ADBench |
2023-08-02 10:01:48 |
| pygood |
0.1 |
Python module providing anomaly detection algorithms |
2023-07-24 09:20:18 |
| roerich |
0.6.0 |
Change point detection. |
2023-07-17 10:48:39 |