Paranom: A Parallel Anomaly Dataset Generator

Justin Gottschlich

Paranom: A Parallel Anomaly Dataset Generator

Machine Learning 2018-01-11 v1 Artificial Intelligence Machine Learning

Authors: Justin Gottschlich

Abstract

In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the classification correctness of LSTM-AD, a state-of-the-art anomaly detection model.

Keywords

pseudorandom number generator anomaly detection

Cite

@article{arxiv.1801.03164,
  title  = {Paranom: A Parallel Anomaly Dataset Generator},
  author = {Justin Gottschlich},
  journal= {arXiv preprint arXiv:1801.03164},
  year   = {2018}
}

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