  Coronal Mass Ejections (CMEs) are arguably the most violent eruptions in the
Solar System. CMEs can cause severe disturbances in the interplanetary space
and even affect human activities in many respects, causing damages to
infrastructure and losses of revenue. Fast and accurate prediction of CME
arrival time is then vital to minimize the disruption CMEs may cause when
interacting with geospace. In this paper, we propose a new approach for
partial-/full-halo CME Arrival Time Prediction Using Machine learning
Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar wind
parameters, we build a prediction engine taking advantage of 182 previously
observed geo-effective partial-/full-halo CMEs and using algorithms of the
Support Vector Machine (SVM). We demonstrate that CAT-PUMA is accurate and
fast. In particular, predictions after applying CAT-PUMA to a test set, that is
unknown to the engine, show a mean absolute prediction error $\sim$5.9 hours of
the CME arrival time, with 54% of the predictions having absolute errors less
than 5.9 hours. Comparison with other models reveals that CAT-PUMA has a more
accurate prediction for 77% of the events investigated; and can be carried out
very fast, i.e. within minutes after providing the necessary input parameters
of a CME. A practical guide containing the CAT-PUMA engine and the source code
of two examples are available in the Appendix, allowing the community to
perform their own applications for prediction using CAT-PUMA.
