Title: Prediction model of a hybrid recurrent neural network based on sequence decomposition

Authors: Jia Zhao; Changxiang Li; Longzhe Han; Yannian Wu; Lieyang Wu

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City, Nanchang Institute of Technology, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City, Nanchang Institute of Technology, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City, Nanchang Institute of Technology, Nanchang, 330099, China ' Shenzhen Guodian Technology Communication Co., Ltd., Shenzhen, 518000, China ' Jiangxi Traffic Monitoring Command Center, Nanchang, 330036, China

Abstract: A large number of random time series exhibit obvious nonlinear characteristics. In order to effectively learn the nonlinear characteristics of time series, this paper proposes a hybrid recurrent neural network (RNN) prediction model based on sequence decomposition. Firstly, this model uses the seasonal trend decomposition method based on local weight regression to decompose the original time series into trend series, seasonal series and residual series, and fuses these three sub-sequences with the original feature series to form a new feature series. Secondly, the input sequence is decomposed into three levels through the progressive decomposition network, and different neural networks are used to predict the decomposition sub-sequences of each level. Finally, the prediction results are spliced and put into the full connection layer for the final prediction. Simulation results show that the proposed model has the lowest prediction error and higher prediction accuracy.

Keywords: sequence decomposition; RNN; recurrent neural network; trend decomposition method; decomposition of network; generalisation.

DOI: 10.1504/IJCSM.2023.134570

International Journal of Computing Science and Mathematics, 2023 Vol.18 No.3, pp.286 - 298

Received: 11 Feb 2023
Accepted: 05 May 2023
Published online: 27 Oct 2023 *

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