搜索资源列表
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运用自回归滑动平均模型进行预测的matlab
程序,The use of autoregressive moving average model to predict the matlab program
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自回归移动平均模型(Autoregressive Integrated Moving Average Model)的Matlab实现,时间序列分析代码-Autoregressive moving average model (Autoregressive Integrated Moving Average Model) to achieve the Matlab
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基于MATLAB的ARIMA模型的源代码。ARIMA模型是自回归滑动平均求和模型,是时间序列分析模型,可以用于时间序列的预测。该代码实现了ARIMA模型的建模和谱分析过程-The ARIMA model based on MATLAB source code. ARIMA model is the sum of autoregressive moving average model is time series analysis models, can be used for time seri
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Adaptive Mean-AutoRegressive-Moving-Average model estimation
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Estimating Adaptive AutoRegressive-Moving-Average-and-mean model
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autoregressive moving average model grade 1,1
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Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA) of real-valued data series using Kalman filter algorithm.
REFERENCE:
A. Schloegl (2000), The electroencephalogram and the adaptive autoregre
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模型包括三种基本类型:自
回归模型、移动平均模型和
自回归移动平均模型
-The model consists of three basic types: the regression model, moving average and autoregressive moving average model
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ARMA model
Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA)
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Parameter identifi cation for a singledegreeoffreedom
system using autoregressive moving average model
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arma(自回归滑动平均模型)的fortran程序-fortran program arma (autoregressive moving average model)
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Matlab计算自回归滑动平均模型参数,自回归滑动模型-Matlab autoregressive moving average model parameters
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受控自回归滑动平均模型的梯度迭代算法,经过多次迭代计算之后,能够有效的逼近真实值-Controlled autoregressive moving average model of gradient iterative algorithm, after several iterations, can effectively approach the true value
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ARIMA模型全称为差分自回归移动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA),-ARIMA is。。。。。
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Parameter identification for a single-degree-of-freedom system using autoregressive moving average model: Application to cutting system
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自回归滑动平均模型,能很好地建立随机风速模型,产生随机序列。用于电力系统可靠性分析。-Autoregressive moving average model, stochastic wind energy is well established model, generate a random sequence. Power system reliability analysis.
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针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构,得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性,对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测,对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。-In order to improve short-term wind speed pr
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用于研究时间序列的方法有AR(自回归)、MA(滑动平均)、ARMA(自回归滑动平均)这三种模型。而对于一个平稳时间序列预测问题,首先要考虑的是寻求与它拟合最好的预测模型。而模型的识别与阶数的确定则是选择模型的关键。
1.用 迭代生成1000个点(前2个点自定义)。
2.在这1000个点中取800点进行时间序列分析建立合适的模型。
3.利用剩余的200个点进行模型预测,并看其是否匹配,最后校正。
-Methods for studying time series are AR (a
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ARMA 模型(Auto-Regressive and Moving Average Model)是研究时间序列的重要方法,由自回归模型(简称AR模型)与滑动平均模型(简称MA模型)为基础“混合”构成。在市场研究中常用于长期追踪资料的研究,如:Panel研究中,用于消费行为模式变迁研究;在零售研究中,用于具有季节变动特征的销售量、市场规模的预测等。(ARMA model is an important method for studying time series. It is composed
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2下载:
自回归移动平均模型(Autoregressive Integrated Moving Average Model)的Matlab实现,时间序列分析代码((Autoregressive moving average model (Autoregressive Integrated Moving Average Model) to achieve the Matlab))
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