  In this paper, we predict severity of extreme weather events (tropical
storms, hurricanes, etc.) using buoy data time series variables such as wind
speed and air temperature. The prediction/forecasting method is based on
various forecasting and machine learning models. The following steps are used.
Data sources for the buoys and weather events are identified, aggregated and
merged. For missing data imputation, we use Kalman filters as well as splines
for multivariate time series. Then, statistical tests are run to ascertain
increasing trends in weather event severity. Next, we use machine learning to
predict/forecast event severity using buoy variables, and report good
accuracies for the models built.
