论文标题
广义自回旋神经网络模型
Generalized Autoregressive Neural Network Models
论文作者
论文摘要
时间序列是一系列观察结果。自回归的集成移动平均线是一类模型，用于时代系列数据。但是，这类模型具有两个关键局限性。它非常适合仅适合相关的线性结构。在这里，我提出了一种新的模型，称为广义自动回归神经网络Garnn。 Garnn是广义线性模型的扩展，其中平均边缘通过将神经网络包含在链路函数中取决于滞后值。该模型的实际应用使用了Zeger和Qaqish（1988）分析的众所周知的脊髓灰质炎病例数。
A time series is a sequence of observations taken sequentially in time. The autoregressive integrated moving average is a class of the model more used for times series data. However, this class of model has two critical limitations. It fits well onlyGaussian data with the linear structure of correlation. Here, I present a new model named as generalized autoregressive neural networks, GARNN. The GARNN is an extension of the generalized linear model where the mean marginal depends on the lagged values via the inclusion of the neural network in the link function. A practical application of the model is shown using a well-known poliomyelitis case number, originated analyzed by Zeger and Qaqish (1988),
