James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection ... error), optimization algorithm (stochastic gradient descent) and learning rate (0.005).

The significance of AI in anomaly detection isn’t merely about finding statistical quirks; it’s about uncovering valuable insights, underlying problems, or opportunities that might otherwise go ...

Abstract: Deep learning models for time series anomaly detection ... these algorithms use a fixed-length window to calculate anomaly scores, leading to inaccuracies and fluctuations. This paper ...

This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series ...