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The reversibility condition for elementary bilinear time-series model based on output sequence alone

机译:仅基于输出序列的基本双线性时间序列模型的可逆性条件

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Reversibility of elementary bilinear time-series model is very important issue in parametric model identification based on minimisation of mean square value of prediction error. The reason is that estimation of parameters of irreversible time-series model is irreversible it is difficult. There is already very well known mathematical reversibility condition expressed as a function of the model's coefficient, which has to be identified. The paper contains a discussion of simple condition, which can provide information about model's reversibility before its identification.
机译:基本双线性时间序列模型的可逆性是基于最小化预测误差均方根值的参数模型识别中非常重要的问题。原因是不可逆时间序列模型的参数估计是不可逆的,这是困难的。已经存在非常知名的数学可逆性条件,必须根据模型系数来表示。本文包含对简单条件的讨论,该条件可以在模型识别之前提供有关模型可逆性的信息。

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