NNreg: Estimate a Single Point with K Nearest Neighbors Regression

In knnwtsim: K Nearest Neighbor Forecasting with a Tailored Similarity Metric

Estimate a Single Point with K Nearest Neighbors Regression

Description

Finds the index of the nearest neighbors for a single point given that point's vector of similarities to all observations eligible to be considered as neighbors. The k.in2 neighbors are identified by their index in the similarity vector, and this index is used to identify the neighbor points in y.in2. The function then returns the mean of the values in y.in2 identified as neighbors. It is suggested to call this function through knn.forecast() for all points to be forecasted simultaneously.

Usage

NNreg(v, k.in2, y.in2)

Arguments

v	numeric vector of similarities used to identify nearest neighbors.
k.in2	integer value indicating the the number of nearest neighbors to be considered.
y.in2	numeric vector of the response series to be forecast.

Value

numeric value of the mean of the k.in2 nearest neighbors in y.in2.

See Also

knn.forecast() the recommended user facing function to perform knn regression for forecasting with NNreg().

Examples

Sim.Mat <- matrix(c(1, .5, .2, .5, 1, .7, .2, .7, 1),
  nrow = 3, ncol = 3, byrow = TRUE
)
Sim.Mat.col <- Sim.Mat[-(3), 3]
y <- c(2, 1, 5)
k <- 2
NNreg(v = Sim.Mat.col, k.in2 = 2, y.in2 = y)

knnwtsim documentation built on March 18, 2022, 6 p.m.