共查询到16条相似文献,搜索用时 78 毫秒
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基于经验模态分解(EMD)的希尔伯特变换(HT),是对非线性时间序列基于EMD进行分解, 然后通过HT获得频谱.利用理想时间序列和青藏高原古里雅冰芯18O时 间序列 ,系统地分析比较了EMD和小波分解(WD)以及HT和小波变换在非线性时间序列处理中的优劣 ,并针对它们各自的缺点提出了可能改进的设想.研究结果表明,将基于EMD的方法和基于WD 的方法有机结合起来应用,可以更有效地识别原时间序列的特征信息.
关键词:
经验模态分解
小波分解
理想时间序列
古里雅冰芯 相似文献
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目前有关非平稳复杂系统及其在预测中的应用研究是一个较少被人理解并有重大科学意义的前瞻性研究课题.在大气运动中,气候正是一个典型的非平稳系统,但是现有的气候预测理论,包括统计预测理论和非线性预测理论,几乎都无一例外地建立在平稳性假定的基础之上,这有悖于气候过程的基本性质,它有可能是导致气候预测水平低下的重要的理论原因.因此以分析如何降低时间序列非平稳程度作为切入点来研究短期气候预测问题有着重要的理论意义.利用基于“升维”思想的支持向量机方法对时变控制参数条件下Lorenz系统产生的非平稳时间序列以及来自实际
关键词:
支持向量机
非平稳过程
预测 相似文献
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目前有关非平稳复杂系统及其在预测中的应用研究是一个较少被人理解并有重大科学意义的前瞻性研究课题.在大气运动中,气候正是一个典型的非平稳系统,但是现有的气候预测理论,包括统计预测理论和非线性预测理论,几乎都无一例外地建立在平稳性假定的基础之上,这有悖于气候过程的基本性质,它有可能是导致气候预测水平低下的重要的理论原因.因此以分析如何降低时间序列非平稳程度作为切入点来研究短期气候预测问题有着重要的理论意义.利用基于“升维”思想的支持向量机方法对时变控制参数条件下Lorenz系统产生的非平稳时间序列以及来自实际 相似文献
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针对风电功率时间序列的混沌特性,提出了一种基于集成经验模态分解(ensemble empirical mode decomposition, EEMD)-近似熵和回声状态网络(echo state network, ESN) 的风电功率混沌时间序列组合预测模型.首先为降低对风电功率局部分析的计算规模以及提高预测的准确性, 利用EEMD-近似熵将风电功率时间序列分解为一系列复杂度差异明显的风电子序列; 然后对各子序列分别建立ESN、经过高频分量正则化改进的EEMD-ESN模型和最小二乘支持向量机预测模型; 最后以某一风电场实际采集的数据为算例,仿真结果表明EEMD-ESN模型在训练速度和预测精度上优于最小二乘支持向量机模型,为实现风电功率短期预测的在线工程应用提供了新的有益参考.
关键词:
混沌时间序列
风电预测
集成经验模态分解
近似熵 相似文献
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时间序列的神经网络预测方法研究 总被引:2,自引:0,他引:2
本文利用动态神经网络可以逼近任意范函这一特性,提出利用此种动态神经网络对时间序列进行预测的方法,并利用设计的网络对某些典型的时间序列进行预测,结果表明利用神经网络对时间序列进行预测有较好的逼近效果。 相似文献
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混沌现象普遍存在于自然界及人类社会中,因此混沌时间序列预测具有重要意义. 提出了一种新的混沌时间序列预测模型------小波回声状态网络,该模型可以有效克服传统回声状态 网络模型中普遍存在的病态矩阵问题,提高了混沌时间序列预测精度.通过对Lorenz、含噪声Lorenz 及间歇式反应釜釜温三个时间序列的预测,将小波回声状态网络与传统回声状态网络进行了比较. 结果表明,小波回声状态网络与传统回声状态网络相比,预测精度提高一倍以上且预测结果更加稳定. 相似文献
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Determining the minimum embedding dimension of nonlinear time series based on prediction method 总被引:1,自引:0,他引:1 下载免费PDF全文
Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding dimension. Then, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series. 相似文献
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Determining the input dimension of a neural network for nonlinear time series prediction 总被引:6,自引:0,他引:6 下载免费PDF全文
Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it ,the paper pressents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Fimally,some validation examples and results are given. 相似文献
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Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations 下载免费PDF全文
The present work reports the development of nonlinear time series prediction method of genetic algorithm(GA) with singular spectrum analysis(SSA) for forecasting the surface wind of a point station in the South China Sea(SCS) with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days(longer for some point station) in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin. 相似文献
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A new method is proposed to determine the optimal embedding
dimension from a scalar time series in this paper. This method
determines the optimal embedding dimension by optimizing the
nonlinear autoregressive prediction model parameterized by the
embedding dimension and the nonlinear degree. Simulation results
show the effectiveness of this method. And this method is applicable
to a short time series, stable to noise, computationally efficient,
and without any purposely introduced parameters. 相似文献
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A method to improve the precision of chaotic time series prediction by using a non-trajectory 下载免费PDF全文
Due to the error in the measured value of the initial state and the
sensitive dependence on initial conditions of chaotic dynamical
systems, the error of chaotic time series prediction increases with
the prediction step. This paper provides a method to improve the
prediction precision by adjusting the predicted value in the course
of iteration according to the evolution information of small
intervals on the left and right sides of the predicted value. The
adjusted predicted result is a non-trajectory which can provide
a better prediction performance than the usual result based on the
trajectory. Numerical simulations of two typical chaotic maps
demonstrate its effectiveness. When the prediction step gets
relatively larger, the effect is more pronounced. 相似文献