Not sure I understand correctly, with rnns you automatically have some encoded non-zero states once you do at least one step. Gated rnns can adapt to treat the first step (or hidden state of zeros) as more important, so they are usable without [non-zero] initial states. If you have multiple timeseries with different a priori states, you can train an encoder network to produce them from some metadata.