基于混沌算子网络的时间序列多步预测研究

结合相空间重构理论和时间序列分析理论,提出一种用于时间序列多步预测的网络模型.网络采用多个混沌算子加权求和的形式构成.网络各层单元采用固定权值连接,混沌算子的控制参数利用混沌优化算法进行训练调节,从而控制预测网络的动力学行为.利用已知时间序列数据构造出训练样本,训练样本在网络训练过程中仅使用一次,促使网络的动力学特性随时间的推移而变化,并逐渐逼近被预测系统的动力学特性,最终完成对未来时刻数据的预测.在对理论数据进行预测分析时,通过计算预测序列的Lyapunov指数验证了预测网络的有效性.在对实际时间序列的预测过程中,该网络表现出了良好的预测性能.仿真结果表明,该预测网络可对多种时间序列在一定的预测步长范围内实现有效的预测.

Multi-step prediction method for time series based on chaotic operator network

Combining the phase space reconstruction theory and the time series analysis theory, a prediction network applied to time series multi-step prediction is proposed. The network is constructed in the weight-sum form of some chaotic operators. Constant connections are adopted among the units. The control parameters of chaotic operators are adjusted by chaos optimization algorithm. The training samples, constructed by known time series data, are used in the training process only once, which makes the dynamic characteristics of the network change and tend to the predicted system with the lapse of time. The validity of the network can be proved by computing the Lyapunov exponent of prediction data. The multi-step predictions for engineering data are also realized by the method. Simulation results prove that the method could validly predict time series when the predictive step is not too long.