Auto.arima没有显示任何顺序 [英] Auto.arima is not showing any order

问题描述

我正在尝试在R中使用auto.arima函数拟合​​arima模型.即使数据不稳定，结果也会显示顺序(0,0,0).

I am trying to fit arima model using auto.arima function in R. The result is showing order (0,0,0) even though the data is non-stationary.

auto.arima(x，approximation = TRUE)

auto.arima(x,approximation=TRUE)

ARIMA(0,0,0)具有非零均值

ARIMA(0,0,0) with non-zero mean

有人可以建议为什么会出现这样的结果吗?顺便说一句，我仅在10个数据点上运行此功能.

Can someone advice why such results are coming? Btw i am running this function on only 10 data points.

推荐答案

10个数据点是用于估计ARIMA模型的非常少的观察值.我怀疑您是否可以据此做出任何明智的估算.此外，估计的模型可能在很大程度上取决于您所查看的时间序列的一部分，并且仅添加很少的观察值就可以显着改变估计的模型的特征.例如:

10 data points is a very low number of observations for estimating an ARIMA model. I doubt that you can make any sensible estimation based on this. Moreover, the estimated model may depend strongly on the part of a time series you looked at and adding only very few observations can change the characteristics of the estimated model significantly. For example:

当我采用只有10个观测值的时间序列时，我还会得到一个ARIMA(0,0,0)模型:

When I take a time series with only 10 observations, I also get a ARIMA(0,0,0) model:

library(forecast)
vec1 <- ts(c(10.26063, 10.60462, 10.37365, 11.03608, 11.19136, 11.13591, 10.84063, 10.66458, 11.06324, 10.75535), frequency = 12)
fit1 <- auto.arima(vec1)
summary(fit1)

但是，如果我使用大约30个观测值，则可以估算出ARIMA(1,0,0)模型:

However, if I use about 30 observations, it an ARIMA(1,0,0) model is estimated:

vec2 <- ts(c(10.260626, 10.604616, 10.373652, 11.036079, 11.191359, 11.135914, 10.840628, 10.664575, 11.063239, 10.755350,
10.158032, 10.653669, 10.659231, 10.483478, 10.739133, 10.400146, 10.205993, 10.827950, 11.018257, 11.633930,
11.287756, 11.202727, 11.244572, 11.452180, 11.199706, 10.970823, 10.386131, 10.184201, 10.209338,  9.544736), frequency = 12)
fit1 <- auto.arima(vec2)
summary(fit1)

如果我使用整个时间序列(413个观测值)，则auto.arima函数会估计"ARIMA(2,1,4)(0,0,1)[12]有漂移".

If I use the whole time series (413 observations), the auto.arima function estimates a "ARIMA(2,1,4)(0,0,1)[12] with drift".

因此，我认为10次观察确实不足以拟合模型.

Thus, I would think that 10 observation is indeed not enough information for fitting a model.

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