I'm a bit frustrated since the time series I am trying to analyse right now has definitely non-stationary curve but it's last values differ greatly from the mean making the time series stationary. According to Dickey-Fuller tests, if I exclude the last 5% of the dataset, the time series is non stationary but with the whole dataset included it's totally stationary according to the test's results.
The orange box in the right part of the plot is the part of the dataset i'm excluding/including.
Should I work with 95% of the dataset?
How should one deals with such kind of problems?
I've attached an image of the time series plot and the results of the Dickey-Fuller tests.
Dickey-Fuller with the whole dataset
- Test Statistic -4.113412
- p-value 0.000920
- no_of_Lags Used 9.000000
- Number of Observations Used 112.000000
- Critical Value (5%) -2.887712
- Critical Value (1%) -3.490131
- Critical Value (10%) -2.580730
Dickey-Fuller with the first 95% of the dataset
- Test Statistic 3.435185
- p-value 1.000000
- no_of_Lags Used 7.000000
- Number of Observations Used 108.000000
- Critical Value (5%) -2.888697
- Critical Value (1%) -3.492401
- Critical Value (10%) -2.581255
Thanks in advance!