Regression turned into classiffication
You actually convert the output of your algorithm from continuous to categorical.

I see many reasons of why you would want to do that. A simple case for this would be when you have very long time-series data that take a lot of space to be saved. In this case, it is convenient to convert the timeseries in a histogram representation, which actually is a number of predefined bins where all the values fall into. This will greatly reduce the memory requirements of your algorithm because you just increase the count of a bin (integer number) when a measurement is taken that falls in it, instead of storing the measurement itself as a float number.

This of course has pitfalls, such as losing the "time" dependencies between the measurements as well as reduction in resolution of your dataset. BUT, it can be really useful when forecasting measurements because instead of having the continuous R space as output of a predictor, you actually have (e.g.) 10 bins (classes) that the next measurement is predicted to fall into, making it easier for your model to be trained.

FYI, this is exactly what is done in the automotive industry.

EDIT - (Added sources)

Sources:

https://people.dsv.su.se/~tony/papers/dmin_2015.pdf (check the algorithm)

https://www.phmsociety.org/node/2284 (the previous algorithm is applied here for trucks)

https://link.springer.com/article/10.1007/s10618-017-0538-6 (another algorithm based on histograms of truck data)