In stationary, if we want to make forecasting, we have to make our data stationary(On classical methods), I get that, but If every data point is independent of each other, how can we make the prediction of what will be the value of the next time step? I mean there is no pattern, no association, nothing. How a model can estimate what will be the next value?
And while this is the case, Imagine I have data that every time stamp has different values (their meaning is completely independent of each other), and this is a classification problem. (I want to classify each row), based on the assumption of "stationary time series", my values are already stationary, so, can I easily convert this data to the "time series data" and forecast it?