luminaire
tods
| luminaire | tods | |
|---|---|---|
| 28 | 3 | |
| 753 | 1,297 | |
| 0.8% | 2.3% | |
| 0.0 | 3.1 | |
| 3 months ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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luminaire
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
What are some alternatives?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
luminol - Anomaly Detection and Correlation library
OpenOOD - Benchmarking Generalized Out-of-Distribution Detection
darts - A python library for user-friendly forecasting and anomaly detection on time series.
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for 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, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data
Merlion - Merlion: A Machine Learning Framework for Time Series Intelligence
timebasedcv - Time based splits for cross validation