tods
luminaire
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| tods | luminaire | |
|---|---|---|
| 3 | 28 | |
| 1,292 | 752 | |
| 3.4% | 1.7% | |
| 3.1 | 0.0 | |
| 8 months ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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tods
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Anomaly detection Algorithms
Sounds that OP is looking for time series anomaly detection, not multivariate. Perhaps https://github.com/datamllab/tods is an option,
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Unsupervised Anomaly Detection with Multivariate Time series
I suggest you try some AutoML library for anomaly detection, e.g.: https://github.com/datamllab/tods
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[P] [R] Luminaire: A hands-off Anomaly Detection Library
TODS
luminaire
What are some alternatives?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
OpenOOD - Benchmarking Generalized Out-of-Distribution Detection
luminol - Anomaly Detection and Correlation library
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
darts - A python library for user-friendly forecasting and anomaly detection on time series.
PyPOTS - A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
timebasedcv - Time based splits for cross validation
Merlion - Merlion: A Machine Learning Framework for Time Series Intelligence