基于组合模型的非线性时间序列预测算法

为解决复杂系统中非线性时间序列预测模型构建效率低、预测精度低的问题,提出基于组合模型的HURST-EMD预测算法.采用EMD算法将非线性时间序列分解为代表原始序列特征的各个IMF,然后引入赫斯特(Hurst)指数将同类的IMF整合为新的分量,最后选用LS-SVR-ARIMA模型进行组合预测.在该算法中,设计了序列分类整合等过程,优化了建模的计算量,构建了高效精准的预测模型.为验证模型的有效性,采用上证指数公共数据集和真实交通流数据进行检验,实验结果表明,改进的基于组合模型的HURST-EMD预测算法在提高预测效率的同时具有更好的预测精度.

A nonlinear time series prediction algorithm based on combination model

In order to solve the problem of low construction efficiency and low accuracy of the nonlinear time series prediction model in complex systems, a Hurst-EMD prediction algorithm based on combination model is proposed. This algorithm uses EMD algorithm to decompose the nonlinear time series into individual IMFs representing the characteristics of the original series, and then introduces Hurst exponent to integrate the similar IMF into new components. Finally, LS-SVR and ARIMA models are used for combinational prediction. In this algorithm, the process of sequence classification and integration is designed, the number of calculation is optimized, and an efficient and accurate prediction model is constructed. In order to verify the validity of the model, the public data set of Shanghai stock index and real traffic flow data are used for testing. The experimental results show that the improved HURST-EMD combination model has better prediction accuracy while improving the prediction efficiency.