  We present RAPID (Real-time Automated Photometric IDentification), a novel
time-series classification tool capable of automatically identifying transients
from within a day of the initial alert, to the full lifetime of a light curve.
Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we
present the first method specifically designed to provide early classifications
of astronomical time-series data, typing 12 different transient classes. Our
classifier can process light curves with any phase coverage, and it does not
rely on deriving computationally expensive features from the data, making RAPID
well-suited for processing the millions of alerts that ongoing and upcoming
wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large
Synoptic Survey Telescope (LSST) will produce. The classification accuracy
improves over the lifetime of the transient as more photometric data becomes
available, and across the 12 transient classes, we obtain an average area under
the receiver operating characteristic curve of 0.95 and 0.98 at early and late
epochs, respectively. We demonstrate RAPID's ability to effectively provide
early classifications of observed transients from the ZTF data stream. We have
made RAPID available as an open-source software package
(https://astrorapid.readthedocs.io) for machine learning-based alert-brokers to
use for the autonomous and quick classification of several thousand light
curves within a few seconds.
