First Differencing to remove seasonality and trends I am trying to remove seasonality and trends from my time series data. I found this post that said to use df_diff = df.diff().diff(12).dropna() (https://www.tobiolabode.com/blog/2020/12/30/how-to-convert-non-stationary-data-into-stationary-for-arima-model-with-python). I don't understand why we need to use diff(12) from pandas. Could someone explain this to me? Thanks!
Since the data is recorded every month (i.e. a data point for each month in the year) and we see a yearly seasonality trend we compare the data for the same month against previous year. This is done by taking the difference against the data from last year (.diff()), which is equal to going back 12 observations since each observation is data for a specific month.