Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode.
Entropy (Basel)
; 23(8)2021 Aug 18.
Article
en En
| MEDLINE
| ID: mdl-34441211
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Entropy (Basel)
Año:
2021
Tipo del documento:
Article
País de afiliación:
China