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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
Update : 2025-03-07 Size : 8kb Publisher : Peter

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ARIMA模型全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA),是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法[1] ,所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA(p,d,q)称为差分自回归移动平均模型,AR是自回归, p为自回归项; MA为移动平均,q为移动平均项数,d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型,是指将非平稳时间序列转化为平稳时间序列,然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归所建立的模型。ARIMA模型根据原序列是否平稳以及回归中所含部分的不同,包括移动平均过程(MA)、自回归过程(AR)、自回归移动平均过程(ARMA)以及ARIMA过程。(To address time consuming and parameter sensitivity in the emerging decomposition- ensemble models, this paper develops a non-iterative learning paradigm without iterative training process.)
Update : 2025-03-07 Size : 1kb Publisher : luc1fer

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))
Update : 2020-05-02 Size : 7kb Publisher : chixiaohang
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