For training AI MLPs to predict time-series data that's known to have sinusoidal behaviors (which might lead to 'reasoning' like it did in LLMs) I bet it's more

For training AI MLPs to predict time-series data that's known to have sinusoidal behaviors (which might lead to 'reasoning' like it did in LLMs) I bet it's more efficient to first curve-fit the data onto continuous data points, and then convert to frequency domain (like you said, FFT), and then do all the training using just "Frequency Domain" datasets. So then the way the AI would "predict" (run inference) would be by spitting out Frequency Domain predictions, which have to be converted back to 'time domain' to get the 'real output'. I'm sure the audio-processing AI systems out there are doing something like this already so it would be interesting to try to leverage that stuff by sending it "audio" that's actually just arbitrary time-series data rather than PCM of sound waves.