R:为什么 AIC 在预测 v6.1 中具有非零均值的 arima 模型中等于 inf,而它在 forecsat v5.8 中具有值
R: why AICs equal to inf in arima models with non-zero mean in forecast v6.1 while it has a value in forecsat v5.8
我已经安装了软件包 forecast v5.8,过了一会儿我将其更新为 forecast v6.1。
我对每小时数据应用了 auto.arima()
函数。但是我在 arima 模型中有 inf,在预测 v6.1 中具有非零均值,而它具有 a 值 在 forecsat v5.8.
为什么会这样?
我只会放一些结果模型
预测 v5.8
>auto.arima(ep_m,approximation = F,stepwise = F,trace = T)
ARIMA(0,0,0)(0,0,1)[24] with non-zero mean : 3693.726
ARIMA(0,0,2)(2,0,0)[24] with non-zero mean : 1712.235
ARIMA(0,0,0)(0,0,2)[24] with non-zero mean : 3292.21
ARIMA(1,0,0)(1,0,0)[24] with non-zero mean : 1450.052
Series: ep_m
ARIMA(2,0,2)(1,0,0)[24] with non-zero mean
Coefficients:
ar1 ar2 ma1 ma2 sar1 intercept
1.8472 -0.9088 -0.7574 0.0879 0.5968 29.5869
s.e. 0.0219 0.0217 0.0479 0.0391 0.0473 0.2868
sigma^2 estimated as 0.3774: log likelihood=-700.47
AIC=1414.94 AICc=1415.1 BIC=1447.23
预测 v6.1
>auto.arima(ep_m,approximation = F,stepwise = F,trace = T)
ARIMA(0,0,0)(0,0,1)[24] with non-zero mean : inf
ARIMA(0,0,2)(2,0,0)[24] with non-zero mean : inf
ARIMA(0,0,0)(0,0,2)[24] with zero mean : Inf
ARIMA(1,0,0)(1,0,0)[24] with non-zero mean : Inf
Series: ep_m
ARIMA(1,0,2)(2,0,0)[24] with non-zero mean
Coefficients:
ar1 ma1 ma2 sar1 sar2 intercept
0.9058 -0.0197 0.0924 0.4766 0.4571 29.2759
s.e. 0.0178 0.0422 0.0396 0.0341 0.0343 2.2677
sigma^2 estimated as 0.3087: log likelihood=-642.71
AIC=1299.41 AICc=1299.57 BIC=1331.7
这给了我一个不同的 最佳 ARIMA。
有关预测包后续版本的更改,请参阅 Changelog。
near-unit-roots 的测试在 v6.0 中变得更加严格,这意味着 Inf AIC 然后返回给一些以前被认为是好的模型。
我已经安装了软件包 forecast v5.8,过了一会儿我将其更新为 forecast v6.1。
我对每小时数据应用了 auto.arima()
函数。但是我在 arima 模型中有 inf,在预测 v6.1 中具有非零均值,而它具有 a 值 在 forecsat v5.8.
为什么会这样? 我只会放一些结果模型
预测 v5.8
>auto.arima(ep_m,approximation = F,stepwise = F,trace = T)
ARIMA(0,0,0)(0,0,1)[24] with non-zero mean : 3693.726
ARIMA(0,0,2)(2,0,0)[24] with non-zero mean : 1712.235
ARIMA(0,0,0)(0,0,2)[24] with non-zero mean : 3292.21
ARIMA(1,0,0)(1,0,0)[24] with non-zero mean : 1450.052
Series: ep_m
ARIMA(2,0,2)(1,0,0)[24] with non-zero mean
Coefficients:
ar1 ar2 ma1 ma2 sar1 intercept
1.8472 -0.9088 -0.7574 0.0879 0.5968 29.5869
s.e. 0.0219 0.0217 0.0479 0.0391 0.0473 0.2868
sigma^2 estimated as 0.3774: log likelihood=-700.47
AIC=1414.94 AICc=1415.1 BIC=1447.23
预测 v6.1
>auto.arima(ep_m,approximation = F,stepwise = F,trace = T)
ARIMA(0,0,0)(0,0,1)[24] with non-zero mean : inf
ARIMA(0,0,2)(2,0,0)[24] with non-zero mean : inf
ARIMA(0,0,0)(0,0,2)[24] with zero mean : Inf
ARIMA(1,0,0)(1,0,0)[24] with non-zero mean : Inf
Series: ep_m
ARIMA(1,0,2)(2,0,0)[24] with non-zero mean
Coefficients:
ar1 ma1 ma2 sar1 sar2 intercept
0.9058 -0.0197 0.0924 0.4766 0.4571 29.2759
s.e. 0.0178 0.0422 0.0396 0.0341 0.0343 2.2677
sigma^2 estimated as 0.3087: log likelihood=-642.71
AIC=1299.41 AICc=1299.57 BIC=1331.7
这给了我一个不同的 最佳 ARIMA。
有关预测包后续版本的更改,请参阅 Changelog。
near-unit-roots 的测试在 v6.0 中变得更加严格,这意味着 Inf AIC 然后返回给一些以前被认为是好的模型。