Analysis of Time Series Anomalies Using Causal InfoGAN and Its Application to Biological Data
Analysis of Time Series Anomalies Using Causal InfoGAN and Its Application to Biological Data
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DOI：
10.1007/978-3-030-32456-8_67
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发表时间：
2019-07
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影响因子：
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通讯作者：
Takaya Ueda;M. Seo;Y. Tohsato;I. Nishikawa
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作者：
Takaya Ueda;M. Seo;Y. Tohsato;I. Nishikawa
数据生成是数据识别的反函数，数据识别是从低维潜在空间到高维数据的映射。正常数据生成器获取其潜在空间中的数据分布，预计会生成任何正常数据，但无法生成异常数据。因此，生成器可用于异常检测。我们提出了一种带有编码器的生成对抗网络（GAN）模型来表征数据异常。 GAN 由普通数据训练，编码器被训练来推断训练生成器的潜在空间。然后，通过组合编码器和生成器，输入到编码器的数据的异常通过数据重构误差及其潜在表示来表征。如果获得了潜在空间的任何可解释的表示，我们就可以通过潜在变量来表征异常。在本研究中，Causal InfoGAN 通过正常时间序列数据进行训练，以获取潜在空间中的时间状态变量。然后，时间异常可以通过状态空间中的异常转变来表征。所提出的方法应用于模拟生物系统中细胞分裂的简单玩具数据，并且观察到在潜在空间中获得了表达正常时间发展的状态转换。
Data generation is an inverse function of the data recognition, which is a map from a low dimensional latent space to a high dimensional data. Generator of normal data acquires the data distribution in its latent space, and is expected to generate any normal data, but unable to generate abnormal data. Therefore, the generator can be used for the anomaly detection. We propose a model of Generative Adversarial Nets (GANs) with an encoder to characterize the data anomaly. GANs are trained by normal data, and the encoder is trained to infer the latent space of the trained generator. Then, by combining the encoder and the generator, anomaly of the data input to the encoder is characterized by the data reconstruction error and by its latent representation. If any interpretable representation is obtained for the latent space, we can characterize the abnormality by the latent variable. In this study, Causal InfoGAN is trained by normal time series data to acquire temporal state variables in the latent space. Then, a temporal abnormality can be characterized by an abnormal transition in the state space. Proposed method is applied to a simple toy data, which mimics a cell division in the biological system, and it is observed that the state transition which expresses the normal time development is obtained in the latent space.