Multistep-Ahead Independent Prediction of Nonlinear Time Series Based on Independent Model

Abstract

Abstract:

A multistep-ahead independent prediction approach of nonlinear time series was proposed. The step-by-step recurrent approach and the independent approach were compared, and the influence of accumulative error on the performance of multistep-ahead prediction was analyzed. The recurrent neural network (RNN) was used to realize the independent prediction approach, and the predictive model of urban rail transit is built, trained and tested by MATLAB. The predictive results showed that the error of independent prediction was smaller than that of step-by-step recurrent approach. The advantages and disadvantages of each approach were analyzed.

Key words：

Key words: nonlinear time series, multistep-ahead independent prediction, accumulative error, recurrent neural network

CLC Number:

TP 273

YANG Zhenming1,YUE Jiguang1,WANG Xiaobao2,XIAO Yunshi1. Multistep-Ahead Independent Prediction of Nonlinear Time Series Based on Independent Model[J]. Journal of Shanghai Jiaotong University, 2013, 47(10): 1626-1631.

References

［1］Badjate S L, Dudul S V. Multi step ahead prediction of North and South hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model［J］. WSEAS Transactions on Information Science and Applications, 2009, 6(4): 684693.

［2］张冬青, 宁宣熙, 刘雪妮. 基于RBF神经网络的非线性时间序列在线预测［J］. 控制理论与应用, 2009, 26(2): 151155.

Zhang D Q, Ning X X, Liu X N. Onling prediction of nonlinear time series using RBF neural networks［J］. Control Theory & Applications, 2009, 26(2): 151155.

［3］Ma Q L, Zheng Q L, Peng H, et al. Multistepprediction of chaotic time series based on coevolutionary recurrent neural network［J］. Chinese Physics B, 2008, 17(2): 536542.

［4］彭金柱, 王耀南, 王杰. 基于递归模糊神经网络的机器人鲁棒H∞跟踪控制［J］. 控制理论与应用, 2010, 27(9): 11451151.

Peng J Z, Wang Y N, Wang J. Robust Hinfinity trackingcontrol for robotic system based on recurrent fuzzyneuralnetworks［J］. Control Theory & Applications, 2009, 6(4): 684693.

［5］杨臻明, 岳继光, 王晓保, 李晓龙. 基于回归模型的城市轨道交通能耗预测［J］. 城市轨道交通研究, 2010, 13(12): 2225.

Yang Z M, Yue J G, Wang X B, Li X L. Prediction of urban rail transit power consumption based on regression model［J］. Urban Mass Transit, 2010, 13(12):2225.

［6］Zemouri R, Gouriveau R, Zerhouni N. Defining and applying prediction performance metrics on a recurrent NARX time series model［J］. Neurocomputing, 2010, 73(1315): 25062521.

［7］Ludvig O, Nunes U. Novel Maximummargin Training Algorithms for Supervised Neural Networks［J］. IEEE Transactions on Neural Networks, 2010, 21(6): 972984.

［8］Cai X D, Zhang N. Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm［J］. Neurocomputing, 2007, 70(1315): 23422353.

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