  Time-domain astronomy (TDA) is facing a paradigm shift caused by the
exponential growth of the sample size, data complexity and data generation
rates of new astronomical sky surveys. For example, the Large Synoptic Survey
Telescope (LSST), which will begin operations in northern Chile in 2022, will
generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky.
The LSST will stream data at rates of 2 Terabytes per hour, effectively
capturing an unprecedented movie of the sky. The LSST is expected not only to
improve our understanding of time-varying astrophysical objects, but also to
reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with
a change of paradigm to data-driven astronomy, the fields of astroinformatics
and astrostatistics have been created recently. The new data-oriented paradigms
for astronomy combine statistics, data mining, knowledge discovery, machine
learning and computational intelligence, in order to provide the automated and
robust methods needed for the rapid detection and classification of known
astrophysical objects as well as the unsupervised characterization of novel
phenomena. In this article we present an overview of machine learning and
computational intelligence applications to TDA. Future big data challenges and
new lines of research in TDA, focusing on the LSST, are identified and
discussed from the viewpoint of computational intelligence/machine learning.
Interdisciplinary collaboration will be required to cope with the challenges
posed by the deluge of astronomical data coming from the LSST.
