Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode |
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Authors: | Jiancheng Sun Zhinan Wu Si Chen Huimin Niu Zongqing Tu |
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Affiliation: | 1.School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China; (S.C.); (H.N.); (Z.T.);2.School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China;3.School of Mathematics and Computer Science, Yichun University, Yichun 336000, China |
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Abstract: | Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent networks for univariate time series. We first trained the VAE to obtain the space of latent probability distributions of the time series and then decomposed the multivariate Gaussian distribution into multiple univariate Gaussian distributions. By measuring the distance between univariate Gaussian distributions on a statistical manifold, the latent network construction was finally achieved. The experimental results show that the latent network can effectively retain the original information of the time series and provide a new data structure for the downstream tasks. |
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Keywords: | time series complex network statistical manifold latent space |
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