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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 single-degree-of-freedom
system using autoregressive moving average model:
Application to cutting system
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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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The code performs the simulation of time series with autoregressive fractionally integrated moving average (ARFIMA) models that generalize ARIMA (autoregressive integrated moving average) and ARMA autoregressive moving average models. ARFIMA models a
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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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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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