Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode.

Jiancheng Sun, Zhinan Wu, Si Chen, Huimin Niu, Zongqing Tu

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jiancheng SunSchool of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.ORCID 0000-0001-9375-8918

Zhinan WuSchool of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Si ChenSchool of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Huimin NiuSchool of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Zongqing TuSchool of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Funding

National Natural Science Foundation of China 62066017

6 · The paper itself

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.

Indexed as

complex networklatent spacestatistical manifoldtime series

Identifiers

PMID34441211

DOI10.3390/e23081071

PMCPMC8394240

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.