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

Mathematics

Xue-Bo Jin¹  Jian-Lei Kong¹  Ting-Li Su¹  Yu-Ting Bai¹  Wen-Tao Gong¹ 

[1] Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China;

关键词: time series data prediction;    long- and short-term memory network;    variational auto-encoder;    normalizing flows;   

DOI  :  10.3390/math10040610

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown