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5下载：
基于GARCH的预测例程，对解决存在异方差的时间序列很好。-GARCH forecasts based on routine, there is heteroscedasticity in solving the time series well.
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matlab编写的基于混沌时间序列的神经网络预测，包括一步和多步预测算法。-matlab prepared chaotic time series based on the neural network to predict, including step and multi-step prediction algorithm.
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matlab格式源代码。功能：径向基神经网络算法源码和应用于时间序列模型建立和预测问题。-matlab source code format. Function: RBF neural network algorithm source code and applies to time-series model and prediction of the problem.
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金融时间序列分析，常用的一些模型分析过程，此仅对ARIMA 做了一些参考-Do time-series.look for some progrom refer to time series of Finalcial data,espeically using ARIMA model.
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对一维时间序列采用盒计数法对其进行多重分形谱的计算-Time series of one-dimensional box-counting method used to carry out its multifractal spectrum calculation
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基于MATLAB生成Mackey-Glass时间序列。首先进行函数的定义，然后运行WORD中的代码即可。-Based on MATLAB to generate Mackey-Glass time series. First definition of function, and then run the code WORD.
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用C-C时间序列插入构造李亚普诺夫计算方法-Time series with CC Insert method of calculating Lyapunov structure
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使用自适应神经模糊推理系统的方法预测时间序列-The use of Adaptive Neuro-Fuzzy Inference System for Prediction of Time Series
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混沌时间序列特征，G_P算法，求关联维，matlan-Characteristics of chaotic time series, G_P algorithm, and correlation dimension, matlan
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利用MATLAB实现一维信号时间序列的，特征提取，其中包括ICA和基于小波包的方法。-Use MATLAB to achieve one-dimensional time series signal, feature extraction, including the ICA and the method based on wavelet packet.
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matlab时间序列分析工具箱，各种时频分析方法的matlab源码。-matlab time series analysis toolbox, various time-frequency analysis methods matlab source.
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典型时间序列模型分析
设有ARMA(2,2)模型，
X（n）+0.3X(n-1)-0.2X(n-2)=W(n)+0.5W（n-1）-0.2W(n-2)
W(n)是零均值正态白噪声，方差为4
（1）用MATLAB模型产生X（n）的500观测点的样本函数，并会出波形；
（2）用你产生的500个观测点估计X（n）的均值和方差；
（3）画出理论的功率谱
（4）估计X（n）的相关函数和功率谱
-Analysis of typical time series model w
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用MATLAB进行时间序列分析,聚类分析和启发式算法一些介绍。个人收集，很有价值-Using MATLAB to carry out time series analysis, clustering analysis and heuristic introduction. Personal collection, great value
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做时间序列预测的svm程序,matlab编的-svm program for time series prediction
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多变量混沌时间序列预测及其在股票市场中的应用 硕士论文-Multivariate Chaotic Time Series Prediction and Its Application in the stock market in the master' s thesis
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Given a time series, the algorithm will test its non-linearity calculating the lyapunov exponent by Jacobi method
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Lyapunov exponent calcullation for ODE-system. The alogrithm employed in this m-file for determining Lyapunov exponents was proposed in A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano, "Determining Lyapunov Exponents from a Time Series," Physi
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为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Ab
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algorithm to calculat the Lyapunov exponent from time series.
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This simulink model simulates the damped driven pendulum, showing it s chaotic motion.
theta = angle of pendulum
omega = (d/dt)theta = angular velocity
Gamma(t) = gcos(phi) = Force
omega_d = (d/dt) phi
Gamma(t) = (d/dt)omega + omega/Q
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