I have a database with hourly records. When I perform my ADF and KPSS test the p-value is less than alpha 0.05 so the series is assumed to be stationary. But by plotting the ACF the delays are all out it shows that the series is not stationary. with two or more differentiation still can't remove the trend and seasonality! someone has already found this problem!
There are different ways to make your time series non-stationary. The most common ways are to take the difference (y_new = y_now - y_before) or take the logarithm of the y variable.
You want to remove stationarity because your model will not be able to capture the trend of the time series because it has never seen similar values before.
In order to deal with seasonality, the easiest way is to include the lags of the y var. Which lags should you include? That will be shown on the Partial Autocorrelation Plot, those lags that are significant.
Another way to deal with the seasonality, that proves to be very effective is to include fourier tranformations of the y var as features/regressors in your data. The fourier transformations have the characteristic to decompose time series into trend, seasonalities (daily, weekly, monthly etc) and the rest.