Low dimensional mid-term chaotic time series prediction by delay parameterized method
Low dimensional mid-term chaotic time series prediction by delay parameterized method
复制标题
延迟参数化方法的低维中期混沌时间序列预测
DOI：
10.1016/j.ins.2019.12.021
复制
发表时间：
2020-04-01
影响因子：
8.1
通讯作者：
Ren, Jingli
中科院分区：
文献类型：
--
作者：
Guo, Xiaoxiang;Sun, Yutong;Ren, Jingli
如何在信息不充分的情况下预测复杂系统的未来行为，即数学上的低维中期混沌时间序列预测，不仅是一个重要的理论问题，而且是一个更为复杂的实际问题。针对这一问题，提出了一种用于低维中期混沌时间序列预测的延迟参数化法。在DPM中，引入了将低维信息嵌入到重构空间中的相关函数，将时间序列与系统的隐序联系起来。采用遍历算法和粒子群算法或遗传算法等智能算法获取预测的最优参数。此外，对Lorenz混沌时间序列、应力应变信号和股票K线图的应用表明，该方法具有较高的预测精度。(C)2019 Elsevier Inc.保留所有权利。
How to predict the future behavior of complex systems with insufficient information, i.e., low dimensional mid-term chaotic time series prediction in mathematical terms, is not only a significant theoretical problem, but a more intricate practical problem. To address this issue, a Delay Parameterized Method (DPM) for low dimensional mid-term chaotic time series forecasting is presented. The correlation function, which immerses the low dimensional information into reconstructed space, is introduced to bridge time series and hidden order of system in DPM. Traversal algorithm and intelligent algorithm including particle swarm optimization or genetic algorithm, are used to obtain the optimal parameters for prediction. In addition, the applications of the proposed method on Lorenz chaotic time series, stress-strain signals and stock K-line maps show that it produces high quality predictions. (C) 2019 Elsevier Inc. All rights reserved.