Stationarity matters because it affects how good our predictions about the future can be. When data isn’t stationary, our predictions might not be very accurate. To check if data is stationary or not, we use two tests: the ADF test and the KPSS test.
The ADF test helps us figure out if the data is changing too much over time. If the result is smaller than a certain number, it means the data is probably okay for making predictions. On the other hand, the KPSS test checks if there’s a pattern or trend in the data. If the result is higher than a specific number, it means there might be a trend that could mess up our predictions.
We also use something called the Autocorrelation Function (ACF) alongside these tests. ACF helps us see how data points relate to each other over time. This helps us pick the right tools to make predictions, especially for models that rely on understanding how data behaved in the past to guess what might happen next.