I am a total beginner with time series analysis. I use R. I understand that time series data need to be stationary for analyses like cross-correlation or modeling.
I am, however, struggling with determining if my data is stationary. I have data sets with air pollution measurements per hour for 2 weeks per data set. I want to analyse the data per 2 weeks and per day.
I could not really say based on the normal plots if the data is stationary or non-stationary.
I plotted the ACFs for both 2 weeks and 1 day both without and with differencing.
For the 2 week period (second plot is differenced data).
For a 1 day period (second plot is differenced data).
I would say (with my very limited knowledge) that the first graphs of both periods do not look stationary, but the differenced data looks like white noise.
I looked a little into the ADF and KPSS test, but my statistical knowledge is not very big, so I do not understand the theory behind it. Also, I do understand how to choose the appropriate k for the ADF test, but when changing k I saw that I can make the p-value lower than 0.05 if I choose the "right" k.
My questions are:
Are the ACF plots of non-differenced data already enough reason to difference the data (because it looks non-stationary)? (taking into account that I am very much a beginner and prefer the easiest method that is acceptable..)
If this is not enough, should I also perform the KPSS and ADF test, and if yes, how should I choose the k for the ADF test?
- Also, I tried to calculate the cross-correlation (with
Ccf()) and found that the differenced data has, on the few instances I tested it on, a lower correlation than the non-differenced data. I would be interested in understanding why this is the case.