|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
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|Msda||34||a year ago||10||March 09, 2021||other||Jupyter Notebook|
|multi-dimensional, multi-sensor, multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector|
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|Anomaly detection in time-series networks. Spatio-temporal Anomaly Detection|
Related Blog: https://towardsdatascience.com/explainable-ai-xai-design-for-unsupervised-deep-anomaly-detector-6bd1275ed3fc
Related Blog: https://ajay-arunachalam08.medium.com/multi-dimensional-time-series-data-analysis-unsupervised-feature-selection-with-msda-package-430900a3829a
Related Blog: https://ajay-arunachalam08.medium.com/multidimensional-multi-sensor-time-series-data-analysis-framework-5c497d8d106b
MSDA is a prototype for unsupervised feature selection and/or unsupervised deep convolutional neural network & lstm autoencoders based real-time anomaly detection from high-dimensional heterogeneous/homogeneous time series multi-sensor data. It also includes a module of Explainable AI for the built time-series anomaly predictor.
Intuitive representation of the unsupervised feature selection is as shown below.
Intuitive representation of the unsupervised real-time point anomalies detection is as shown below.
From local explanations to global understanding with explainable AI for trees - motivation from here - https://www.nature.com/articles/s42256-019-0138-9
, Image credits - slundberg/shap
MSDA is an open source
low-code Multi-Sensor Data Analysis library in Python that aims to reduce the hypothesis to insights cycle time in a time-series multi-sensor data analysis & experiments. It enables users to perform end-to-end proof-of-concept experiments quickly and efficiently. The module identifies events in the multidimensional time series by capturing the variation and trend to establish relationship aimed towards identifying the correlated features helping in feature selection from raw sensor signals. Also, it provides a provision to precisely detect the anomalies in real-time streaming data an unsupervised deep convolutional neural network & also a lstm autoencoders based detectors are designed to run on GPU/CPU. Finally, a game theoretic approach is used to explain the output of the built anomaly detector model.
The package includes:-
easy to use and
** Deep Convolutional Neural Network **
inspiration from this IEEE paper - https://ieeexplore.ieee.org/document/8581424
** LSTM Autoencoder **
inspiration from here - https://www.nature.com/articles/s41598-019-55320-6
The easiest way to install msda is using pip.
pip install msda
$ git clone https://github.com/ajayarunachalam/msda $ cd msda $ python setup.py install
!pip install msda
Follow the rest as demonstrated in the demo example for Unsupervised Feature Selection [here] -- https://github.com/ajayarunachalam/msda/blob/main/demo.ipynb
Follow the rest as demonstrated in the demo example for Unsupervised Deep Anomaly Detectors & Time series predictor as Explainable AI [here] -- https://github.com/ajayarunachalam/msda/blob/main/demo1_v1.ipynb
Most of the dependencies are installed automatically. But, if not installed when you install MSDA, then these dependencies must be installed as shown below.
pip install pandas pip install numpy pip install matplotlib pip install datetime pip install statistics pip install torch pip install seaborn pip install sklearn pip install scipy pip install shap pip install keras pip install ipywidgets
Installation is only supported on 64-bit version of Python. Tested on numpy version '1.18.3', pandas <= '1.0.5', torch == 1.4.0, keras == 2.0.8, seaborn = '0.9.0', shap = '0.39.0', ipywidgets == 7.5.1
MSDA is an open source library that anybody can use. In our view, the ideal target audience of MSDA is:
Copyright 2021 Ajay Arunachalam [email protected]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 2021 GitHub, Inc.