More broadly I think this is an issue with a narrow definition of "time series", aside from the DB angle. When I was doing more forecasting and predictive modeling, I was constantly stymied by "time series" resources only considering univariate time series, where my problems were always extremely multivariate (and also rarely uniformly sampled...).
When I've asked around for other vocabulary here, the options are slim. Panel data can work, but that has more to do with there being both a time and spatial dimension (e.g. group, cohort, user, etc.) than there being multiple metrics observed over time. It's also an unfamiliar term for data scientist folks without a traditional stats background. "Multivariate time series" might be technically correct, but that works much better in the modelling domain than the database domain.