Why should I do a Time Series Classification instead of a Time Series Regression? I'm doing a project where I'm tasked with predicting "growth coefficents" (classes representing sales at a year from now as a percentage of current sales) for sales of different products, given a time series of the product sales during the last month. What I'm not understanding is what the usage is of getting a class telling me that sales will be 50% of current sales next year, instad of doing a regression and getting actual sales number. I understand that a specific number will not be that reliable considering the given time series is much shorter than the one that would be predicted - but could one just not infer from this somewhat unreliable number that sales would be "about 50%" and get the same answer as one would get from a classification? Or would this "inferred class" from a regression be less reliable than the class predicted from a classification model?
I'm a total beginner when it comes to ML, Time Series and this forum, so I hope I got everything right when posting this question! :)

You are absolutely correct: You should be doing a regression and using that information to predict future sales. If you need to calculate a probability (e.g. probability of 50% decline) the best way to do it is by fitting a distributional model to the errors in your regression. Trying to build a classifier for this is a very bad idea that throws away a huge chunk of the information available to you.
