  The forecast of the time of arrival of a coronal mass ejection (CME) to Earth
is of critical importance for our high-technology society and for any future
manned exploration of the Solar System. As critical as the forecast accuracy is
the knowledge of its precision, i.e. the error associated to the estimate. We
propose a statistical approach for the computation of the time of arrival using
the drag-based model by introducing the probability distributions, rather than
exact values, as input parameters, thus allowing the evaluation of the
uncertainty on the forecast. We test this approach using a set of CMEs whose
transit times are known, and obtain extremely promising results: the average
value of the absolute differences between measure and forecast is 9.1h, and
half of these residuals are within the estimated errors. These results suggest
that this approach deserves further investigation. We are working to realize a
real-time implementation which ingests the outputs of automated CME tracking
algorithms as inputs to create a database of events useful for a further
validation of the approach.
