Are there any consequences to having negative values in your time series training dataset? I haven't read anything that says you shouldn't have negative values in your time series training dataset, so I was wondering if there are any consequences for having negative values in my training dataset?

Many time series models assume Gaussian error terms, meaning that all real numbers are possibilities. In that sense, negative values are fine. Those negative values might indicate undesirable outcomes (e.g., losses instead of profits), but you might be modeling something that sometimes has such events (e.g., stock returns).
If you’re in a framework that assumes positive values (modeling counts or distances, for instance), then you have a problem.
