afraniomelo/KydLIB
Routines for exploratory data analysis.
This tool helps process system engineers understand complex time series data from industrial processes. You input raw process data, and it generates visualizations and metrics for correlations, autocorrelations, signal-to-noise ratios, and multivariate Gaussianity. This is for engineers and scientists who need to diagnose issues or monitor system health within chemical engineering or similar fields.
No commits in the last 6 months. Available on PyPI.
Use this if you work with time series data from industrial processes and need to quickly explore its characteristics to identify patterns, relationships, or anomalies.
Not ideal if you need a general-purpose statistical analysis tool for non-time series data or advanced machine learning model development.
Stars
29
Forks
5
Language
Python
License
MIT
Category
Last pushed
Apr 13, 2023
Commits (30d)
0
Dependencies
9
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