  In this paper, we propose two nonparametric methods used in the forecasting
of functional time-dependent data, namely functional singular spectrum analysis
recurrent forecasting and vector forecasting. Both algorithms utilize the
results of functional singular spectrum analysis and past observations in order
to predict future data points where recurrent forecasting predicts one function
at a time and the vector forecasting makes predictions using functional
vectors. We compare our forecasting methods to a gold standard algorithm used
in the prediction of functional, time-dependent data by way of simulation and
real data and we find our techniques do better for periodic stochastic
processes.
