I am trying to use the Augmented Dickey Fuller test to see if the given time series data are stationary or not.
As a dirty/quick method, I did the following: 1) plotted the time series data to see if they look stationary or not, and 2) split the data into two and compare mean and variance and if they are similar to each other.
Both methods make me think that the data is stationary. However, the augmented dickey fuller test statistic is high and I cannot reject the null hypothesis.
I was just wondering if it's because I violated assumptions of the test? Unfortunately, I couldn't really find assumptions of the test on the internet. I would greatly appreciate if someone can help me this.