“来自 CSS 的非平稳季节性 AR 部分"R中的错误 [英] "non-stationary seasonal AR part from CSS" error in R

问题描述

我正在尝试拟合季节性分解系列的 ARIMA 模型.但是当我尝试执行以下操作时:

I am trying to fit ARIMA model of a seasonally decomposed series. But when I try to execure following:

fit = arima(diff(series), order=c(1,0,0),
   seasonal = list(order = c(1, 0, 0), period = NA))

它给了我以下错误:

arima(diff(series), order = c(1, 0, 0), season = list(order) 中的错误= c(1, :来自 CSS 的非平稳季节性 AR 部分

Error in arima(diff(series), order = c(1, 0, 0), seasonal = list(order = c(1, : non-stationary seasonal AR part from CSS

出了什么问题，错误是什么意思?

what is wrong and what does the error mean?

推荐答案

当使用 CSS(条件平方和)时，自回归系数可能是非平稳的(即，它们落在平稳过程的区域之外).对于您正在拟合的 ARIMA(1,0,0)(1,0,0)s 模型，两个系数都应介于 -1 和 1 之间，过程才能保持平稳.

When using CSS (conditional sum of squares), it is possible for the autoregressive coefficients to be non-stationary (i.e., they fall outside the region for stationary processes). In the case of the ARIMA(1,0,0)(1,0,0)s model that you are fitting, both coefficients should be between -1 and 1 for the process to be stationary.

您可以通过使用参数 method="ML" 来强制 R 使用 MLE(最大似然估计).这速度较慢，但​​提供了更好的估计，并且总是返回一个平稳的模型.

You can force R to use MLE (maximum likelihood estimation) instead by using the argument method="ML". This is slower but gives better estimates and always returns a stationary model.

如果您要对系列进行差分(就像您在这里一样)，通常最好通过模型而不是明确地做到这一点.所以你的模型会更好地估计使用

If you are differencing the series (as you are here), it is usually better to do this via the model rather than explicitly. So your model would be better estimated using

set.seed(1)
series <- ts(rnorm(100),f=6)
fit <- arima(series, order=c(1,1,0), seasonal=list(order=c(1,0,0),period=NA), 
         method="ML")

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