mononitogoswami/tsad-model-selection
Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.
This project helps operations engineers and data scientists identify the best anomaly detection model for their time-series data without needing labeled examples. It takes in raw time-series datasets and outputs a ranking of anomaly detection models, indicating which one is most likely to perform best. This is especially useful for anyone dealing with vast amounts of sensor data, system logs, or financial streams where manual labeling is impractical.
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Use this if you have time-series data and need to find the most effective anomaly detection model among many options, but lack the labeled anomaly data traditionally required for model selection.
Not ideal if you already have a method for generating labeled anomaly data or if you need to select models for non-time-series data.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Dec 14, 2023
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