astha-chem/mvts-ano-eval
A repository for code accompanying the manuscript 'An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series' (published at TNNLS)
This project helps operations engineers, reliability analysts, or data scientists pinpoint unusual behavior in complex systems that generate many data points over time. You provide historical operational data from systems like water treatment plants or server machines, and it helps you find anomalies. This is especially useful for identifying equipment malfunctions or security incidents early.
108 stars. No commits in the last 6 months.
Use this if you need to compare how effectively different deep learning methods can detect and diagnose anomalies in multivariate time series data from industrial control systems, smart infrastructure, or server monitoring.
Not ideal if you are looking for a simple, off-the-shelf anomaly detection solution without needing to evaluate multiple deep learning architectures or if your data is not in a time series format.
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108
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40
Language
Python
License
MIT
Category
Last pushed
May 09, 2023
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