Irregular time series are not handled that well by RNNs in general, if disparity is big. Timestamps are not good, as they’re absolute; even if you shift to zero base - you’ll get position dependent forecasts, inferior beyond your training length. Time differences (like numpy.diff) should work better, another approach is adding “periodic” features {sin,cos}(2pit/period).