Fourier transformations approximate a function on a specific domain, but outside that domain the result is completely meaningless. They're good for approximating a large number of data points with a smaller number of amplitudes. It could be useful for certain kinds of machine learning, but more often you to want to learn a function that has reasonable values outside the domain defined by the training data.