Intelligent Sensing Toolbox for Multivariate Time Series
Project description
intelligent-sensing-toolbox
Intelligent Sensing Toolbox (Isensing) is a Python package that focuses on multivariate time series analysis. This toolbox includes multiple open-source machine learning algorithms and statistic calculations.
In data analytics, making sense of massive numbers of data requires machine learning to work on datasets from different multiple sources in order to generate insights. For situation where a node that generates data points of multiple features in time series, massive number of nodes will make analysis more challenging.
Isensing provides a list of algorithms that does features extraction, decomposition and anomaly detections.
Installation
Isensing is built upon Python 3. To install Isensing, make sure Python 3 and pip is installed.
pip install isensing
Dependencies
pandas
numpy
scipy
sklearn
statsmodels
matplotlib
plotly
shapely
These dependencies will be installed automatically using pip.
Modules
anomaly
# class
AlphaHull
HDR
# functions
outlier_detection()
isensing_anomalies()
decomposition
# class
RobustPCA
features_extraction
# functions
multiple_regression()
fast_DTW()
pearsonr_correlation()
Tutorial
References
- https://github.com/robjhyndman/anomalous-acm
- http://blog.thehumangeo.com/2014/05/12/drawing-boundaries-in-python/
- https://feb.kuleuven.be/public/u0017833/Programs/pca/robpca.txt
License
Apache License 2.0
Project details
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