Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data.

翻译：时间序列数据中的异常检测旨在识别偏离正常行为的数据点，这是制造业、医学影像和网络安全等多个领域的常见问题。近年来，生成对抗网络（GANs）在处理时间序列异常检测方面展现出有效性。GANs的神经网络架构（即生成器和判别器）能够显著提升异常检测的准确性。本文提出一种新型GAN模型——调整型LSTM生成对抗网络（ALGAN），该模型通过调整LSTM网络的输出，在无监督环境下改进单变量和多变量时间序列数据的异常检测性能。我们基于46个真实世界单变量时间序列数据集和一个跨领域的大型多变量数据集对ALGAN进行评估。实验表明，ALGAN在时间序列异常检测任务中优于传统方法、基于神经网络的方法以及其他基于GAN的方法。