With a time series model, we need to achieve stationarity with our model before fitting an arima model. One way of achieving this is to diff our data so that we are removing the trend in the data and just fitting our model to the difference.
If I want to predict on the training data, then I can use my parameters get my predictions, and then undiff by using the first value for the target observation and adding the subsequent diffs.
My question is this: Say I'm doing a kaggle competition where I want to use an Arima model to predict the test data. Then I won't have an initial test label to add my diff value to. So how do can I turn the outputs from my arima model into actual predictions after I made my data stationary?