Hello: I've downloaded this dataset, and when I plot it it is clearly
non-stationary
df <- read.csv('
https://raw.githubusercontent.com/ourcodingclub/CC-time-series/master/monthly_milk.csv
')
plot(df,type="l")
But when I apply the Augmented Dickie-Fuller Test I get a p value of 0.01,
implying that there is evidence to reject the null that the series is
non-stationary. I am puzzled as to why this is happening. Is this because
the confidence level is basically too high or is something else going on?
adf.test(df[,2])
Augmented Dickey-Fuller Test
data: df[, 2] Dickey-Fuller = -9.9714, Lag order = 5, p-value = 0.01
alternative hypothesis: stationary
Thanks Nick Wray
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