boniolp/MSAD
[VLDB 2023] Model Selection for Anomaly Detection in Time Series
This project helps data scientists, machine learning engineers, and researchers working with time series data to choose the best anomaly detection model for their specific dataset. It takes raw time series and their associated anomaly scores as input and outputs a recommendation for the most effective anomaly detection method. The end-user is anyone needing to reliably identify unusual patterns in their time-series data.
Use this if you need to select the most effective anomaly detection model for your time series data and want to avoid trial-and-error, ensuring optimal performance.
Not ideal if you are looking for new anomaly detection algorithms themselves, rather than methods for selecting existing ones.
Stars
43
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 02, 2025
Commits (30d)
0
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