The TVS Task Force has apparently become active but is approaching the problem by considering a small handful of object types, which I do not think is the best approach.

I note that the winning entry to PLAsTiCC used GPR to flexibly parameterize lightcurve behavior (e.g., the existing and measurement of peaks) in a way that may be useful when considering non-periodic features for transients.

I note that there may be some tension here between providing precomputed features that are valuable as inputs to machine-learned models, and features that may be more interpretable and hence useful for human querying and filtering of DIAObjects.

(continuing to capture notes here: some transient-specific features in http://adsabs.harvard.edu/abs/2019arXiv190400014M)

Noting here because I didn't realize it earlier: LDM-151 section 6.21 has an enumerated set of baselined periodic and non-periodic features (again, derived from Butler et al. 2011).

There's no way you could have known this but, FYI: the attached ascii file MLG_email_TVS_VarChar.txt has a list of everywhere I could find that DM documentation has content related to variability parameters.