Just spitballing here, about an application of the Laplace transform. We have a product that allows the users to use machine learning in a semi-automatized way,

Just spitballing here, about an application of the Laplace transform. We have a product that allows the users to use machine learning in a semi-automatized way, without deeply understanding hyperparameter optimization, model testing, selection and evaluation and such. There was some talk about supporting the prediction of time-series data. I have absolutely no knowledge of how time-series data should be pre-processed and what kind of algorithms are common or applicable in general. (I'm not in charge of the R&D of the data-science-y features) However, it seems like Laplace transform as a pre-processing step ticks a lot of the checkboxes. As a superset of Fourier, it supports periodic changes in time series, and being about exponentials, it also allows for growth (or decreasing) over time, allowing to transform a time series to data that is more applicable to classical ML algorithms. Is Laplace transform actually used for such usecases?