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Time Series Anomaly Detection with PyFBAD
An End-to-End Unsupervised Outlier DetectionTaken from Unsplash
The typical flow of a machine learning project starts with reading the data, followed by some preprocessing, training, testing, visualization, and sharing the results with the notification system. Of course, all the steps can be easily done with the help of various open-source libraries. However, in some task-specific cases, such as anomaly detection in time series data, reducing the number of library and hard-coded steps would be more beneficial for explainability. The pyfbad library has been developed for that reason.