KDD-OpenSource/DeepADoTS
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
This project helps data scientists, machine learning engineers, and researchers benchmark deep learning methods for detecting anomalies in time series data. It takes in various time series datasets and outputs anomaly scores for each data point, indicating how unusual it is. You can then use these scores to identify unusual patterns or events within your data.
597 stars. No commits in the last 6 months.
Use this if you need to systematically compare the performance of different deep learning anomaly detection algorithms on your time series data.
Not ideal if you are looking for a plug-and-play solution without needing to understand or compare different deep learning models.
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597
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115
Language
Python
License
MIT
Category
Last pushed
May 25, 2022
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