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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). ACF plot of data of 2 week period; second plot is differenced data

For a 1 day period (second plot is differenced data). ACF plot of data of 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:

  1. 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..)

  2. 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?

EDIT:

  1. 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.

Thanks!

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Unnecessary differencing or filtering can inject structure (see Slutsky Effect) . Sometimes a series can have a shift in the mean causing "non-statioanarity" ..the correct remedy is to neither difference or de-trend but to "de-mean" or use a Level Shift variable/filter to render the residual series stationary.

Sometimes there is more than 1 trend requiring a number of trend variables/filters .... none of which have to start at the beginning if the series. Analysis will tell you which of these three approaches

differencing de-meaning de-trending are suitable for your data.

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