Paranom: A Parallel Anomaly Dataset Generator

TL;DR

Paranom is a parallel dataset generator designed to enhance anomaly detection models like LSTM-AD by providing diverse and extensive training data, thereby improving classification accuracy.

Contribution

This paper introduces Paranom, a novel parallel anomaly dataset generator that improves anomaly detection performance by augmenting training data.

Findings

Paranom improves LSTM-AD classification accuracy.

Experimental results demonstrate dataset generator's effectiveness.

Enhances anomaly detection robustness.

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.