Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode
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 univ...
Main Authors: | Jiancheng Sun, Zhinan Wu, Si Chen, Huimin Niu, Zongqing Tu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/23/8/1071 |
Similar Items
-
Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy
by: Jiancheng Sun
Published: (2020-01-01) -
Statistical analyses of some latent variable models.
Published: (1999) -
Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology
by: Huimin Liu, et al.
Published: (2018-04-01) -
Three Contributions to Latent Variable Modeling
by: Liu, Xiang
Published: (2019) -
Multiple comparison procedures for a latent variable model with bivariate ordered categorical responses.
Published: (2012)