Unsupervised Anomaly Detection on Time Series Data

In this project, we aimed at improving a classifier’s performance using the estimated likelihood distribution of a training dataset. The study involved injecting noise into datasets, training classifiers with varying noise levels, estimating data density using unsupervised methods, and exploring relationships between classifier scores, losses, and density estimates. The approach was tested on mixed datasets and financial data, adjusting the classifier's behavior based on density estimates.

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