ALGAN：基于调整LSTM生成对抗网络的时间序列异常检测
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
AI总结：
本文提出ALGAN模型，通过调整LSTM输出改进GAN架构，在无监督下有效检测单变量和多变量时间序列异常，并在46个单变量及一个大型多变量数据集上超越传统及基于GAN的方法。
摘要：
时间序列数据中的异常检测，即识别偏离正常行为的数据点，是制造、医学成像和网络安全等多个领域中常见的问题。近年来，生成对抗网络（GANs）被证明在时间序列数据的异常检测中有效。GANs的神经网络架构（即生成器和判别器）能够显著提高异常检测的准确性。在本文中，我们提出了一种新的GAN模型，命名为调整LSTM生成对抗网络（ALGAN），该模型调整LSTM网络的输出，以在无监督设置下改进单变量和多变量时间序列数据的异常检测。我们在46个真实世界的单变量时间序列数据集和一个跨越多个领域的大型多变量数据集上评估了ALGAN的性能。我们的实验表明，ALGAN在时间序列数据的异常检测中优于传统的、基于神经网络的以及其他基于GAN的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
