固定测试问题 [英] Stationary Test issue

问题描述

我正在使用航空里程数据集，并进行了三种不同的测试以检查时间序列数据集中的平稳性

I am working with air miles data set and i conducted three different tests to check for stationary in the time series data set

测试1:使用acf和pacf

Test 1: Using acf and pacf

acf(airmiles)

pacf(airmiles)

在区分之后，现在大多数值都位于显着性水平

After differentiating its seems most of the values lies in significance level now

acf(diff(airmiles))
pacf(diff(airmiles))

测试2:使用adf.test

Test 2: Using adf.test

adf.test(airmiles,k=0,alternative = "stationary")


    Augmented Dickey-Fuller Test

data:  airmiles
Dickey-Fuller = -1.1415, Lag order = 0, p-value = 0.8994
alternative hypothesis: stationary

p值似乎大于0.05，所以我区分然后进行相同的测试

p-value seems to be greater than 0.05 so i differentiate and then conduct same test

adf.test(diff(airmiles),k=0,alternative = "stationary")


    Augmented Dickey-Fuller Test

data:  diff(airmiles)
Dickey-Fuller = -5.4406, Lag order = 0, p-value = 0.01
alternative hypothesis: stationary

因此现在值较小，但在kpss.test情况下

and so value is less now but in case of kpss.test

kpss.test(diff(airmiles))   KPSS Test for Level Stationarity

data:  diff(airmiles) KPSS Level = 0.83442, Truncation lag parameter = 1, p-value = 0.01

p值已经小于0.05，我担心我应该实际使用哪些测试，最后哪种测试会导致更好的模型.

The p-value is already less than 0.05 and i am concerned about which tests should i actually work with and which one leads to a better model at the end.

推荐答案

一个具有AR1和2个离群​​值且分别处理期间10和22的差分模型将是一个很好的模型.请注意，没有常量.

A differencing model with an AR1 and 2 outliers dealing with period 10 and 22 would be a good model. Notice there is no constant.

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