  A novel method for predicting time series is described and demonstrated. This
method inputs time series data points and outputs multiple "spaghetti"
functions from which predictions can be made. Spaghetti prediction has
desirable properties that are not realized by classic autoregression, moving
average, spline, Gaussian process, and other methods. It is particularly
appropriate for short time series because it allows asymmetric prediction
distributions and produces prediction functions which are robust in that they
use multiple independent models.
