PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

Xue-Bo Jin, Wen-Tao Gong, Jian-Lei Kong, Yu-Ting Bai and Ting-Li Su
Additional contact information
Xue-Bo Jin: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Wen-Tao Gong: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Yu-Ting Bai: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Ting-Li Su: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China

Mathematics, 2022, vol. 10, issue 4, 1-17

Abstract: Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.

Keywords: time series data prediction; long- and short-term memory network; variational auto-encoder; normalizing flows (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/4/610/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/4/610/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:4:p:610-:d:751227

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().