基于循环步长跳跃网络的时间序列预测算法

TIME SERIES PREDICTION OF CYCLE RESERVOIR WITH STEP JUMPS NETWORK

  • 摘要: 传统基于回声状态网络的混沌时间序列预测存在网络结构不确定、储备池内部结构冗余的问题,造成网络预测精度低。针对上述问题,提出一种改进的确定性循环跳跃网络。该文构建单向环形连接的拓扑结构,并共享连接权值,避免储备池中随机连接造成的网络不稳定性,从而提升预测精度;设计双向步长跳跃模式,减少网络内部连接的冗余,降低储备池的复杂度,有效地提高网络构建的速度。在混沌时间序列上短期预测的实验结果表明,所提出算法在混沌时间序列的单步预测中具有更好的性能。

     

    Abstract: The traditional chaotic time series prediction based on echo state network has the problems of uncertain network structure and redundant internal structure of the reserve pool, resulting in low network prediction accuracy. To solve these problems, an improved deterministic cyclic hopping network is proposed. The topology of unidirectional ring connection was constructed, and the connection weights were shared to avoid network instability caused by random connections in the reserve pool and ensure the improvement of prediction accuracy. We designed a bidirectional step hopping mode to reduce the redundancy of network internal connections, reduced the complexity of the reserve pool, and effectively improved the speed of network construction. The experimental results of short-term prediction on chaotic time series show that the proposed algorithm has better performance in one-step prediction of chaotic time series.

     

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