| Name | Version | Summary | date |
| nonconform |
0.91.0 |
Conformal Anomaly Detection |
2025-10-09 12:51:29 |
| unquad |
0.8.5 |
DEPRECATED: This package has been renamed to 'nonconform'. Please install 'nonconform' instead with `pip install nonconform`. Conformal Anomaly Detection |
2025-07-15 12:27:38 |
| graphomaly |
0.3.2 |
Anomaly detection in graphs modeling financial transactions and computer networks. |
2025-07-13 23:34:56 |
| pyclad |
0.2.0 |
Python Library for Continual Lifelong Anomaly Detection |
2024-10-27 12:16:13 |
| spade-anomaly-detection |
0.3.3 |
Semi-supervised Pseudo Labeler Anomaly Detection with Ensembling (SPADE) is a semi-supervised anomaly detection method that uses an ensemble of one class classifiers as the pseudo-labelers and supervised classifiers to achieve state of the art results especially on datasets with distribution mismatch between labeled and unlabeled samples. |
2024-09-04 21:02:43 |
| momentfm |
0.1.1 |
MOMENT: A Family of Open Time Series Foundation Models |
2024-08-09 02:39:19 |
| coupled-biased-random-walks |
2.1.1 |
Outlier detection for categorical data |
2024-07-11 00:19:42 |
| fedot-ind |
0.4.1.2 |
Automated machine learning framework for time series analysis |
2024-03-07 12:51:52 |
| 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 |
| 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 |