  The R package knnwtsim provides functions to implement k nearest neighbors
(KNN) forecasting using a similarity metric tailored to the forecasting problem
of predicting future observations of a response series where recent
observations, seasonal or periodic patterns, and the values of one or more
exogenous predictors all have predictive value in forecasting new response
points. This paper will introduce the similarity measure of interest, and the
functions in knnwtsim used to calculate, tune, and ultimately utilize it in KNN
forecasting. This package may be of particular value in forecasting problems
where the functional relationships between response and predictors are
non-constant or piece-wise and thus can violate the assumptions of popular
alternatives. In addition both real world and simulated time series datasets
used in the development and testing of this approach have been made available
with the package.
