Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network

IEEE Access

Wei Wang¹  Linqing Wang¹  Ying Liu¹  Jun Zhao¹  Qiang Wang¹ 

[1] School of Control Sciences and Engineering, Dalian University of Technology, Dalian, China;

关键词: Deep learning;    echo state network;    incomplete dataset;    prediction;    time series;   

DOI  :  10.1109/ACCESS.2019.2948367

来源: DOAJ

【 摘 要 】

In the complex industrial environment, data missing situation is often occurred in the process of data acquisition and transition. The major contribution of the paper is the proposal of a deep bidirectional echo state network (DBESN) framework for time series prediction with such incomplete dataset. Instead of data imputation methodology, a bidirectional fusion reservoir is here designed to extract the deep bidirectional feature along with forward and backward time scales, based on which a deep autoencoder echo state network (DAESN) and a deep bidirectional state echo state network (DBSESN) are constructed for the incomplete output and input samples, respectively. As for such two networks, a bidirectional echo state network (BESN) is proposed for connecting them to constitute the DBESN framework for prediction. To verify the effectiveness of the proposed method, one synthetic time series as well as two real-world industrial datasets are employed to conduct the comparative experiments. The experimental results demonstrate that the proposed method outperforms other comparative ones at various missing rates.

【 授权许可】

Unknown