  Three methods of temporal data upscaling, which may collectively be called
the generalized k-nearest neighbor (GkNN) method, are considered. The accuracy
of the GkNN simulation of month by month yield is considered (where the term
yield denotes the dependent variable). The notion of an eventually well
distributed time series is introduced and on the basis of this assumption some
properties of the average annual yield and its variance for a GkNN simulation
are computed. The total yield over a planning period is determined and a
general framework for considering the GkNN algorithm based on the notion of
stochastically dependent time series is described and it is shown that for a
sufficiently large training set the GkNN simulation has the same statistical
properties as the training data. An example of the application of the
methodology is given in the problem of simulating yield of a rainwater tank
given monthly climatic data.
