% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/mstep.R

\name{KNN.acf}

\alias{KNN.acf}

\title{KNN.acf}

\usage{

KNN.acf(X, Y, X.ts, k = 10, dist = "euclidean", C = 2, F = 0, Acf, Pacf,

TS, del = 0)

}

\arguments{

\item{X:}{training input [N,n]}

\item{Y:}{training output [N,m]}

\item{X.ts:}{test input [N.ts,n]}

\item{k:}{min number of neighbours}

\item{dist:}{type of distance: \code{euclidean, cosine}}

\item{F:}{forgetting factor}

\item{C:}{integer parameter which sets the maximum number of neighbours (Ck)}

\item{wta:}{if TRUE a winner-takes-all strategy is used; otherwise a weigthed combination is done on the basis of the l-o-o error}

\item{Acf:}{autocorrelation function of the training series}

}

\value{

vector of N.ts predictions

}

\description{

Multioutput KNN

}

\details{

KNN.acf

Multioutput KNN for multi-step-ahed prediction. It performs a locally constant model with weighted combination of local model on the basis of the dynamic properties of the training time series.

}

\examples{

## Multi-step ahead time series forecasting

library(lazy)

t=seq(0,200,by=0.1)

N<-length(t)

H<-500 ## horizon prediction

TS<-sin(t)+rnorm(N,sd=0.1)

TS.tr=TS[1:(N-H)]

N.tr<-length(TS.tr)

TS.ts<-TS[(N-H+1):N]

TS.tr=array(TS.tr,c(length(TS.tr),1))

E=MakeEmbedded(TS.tr,n=3,delay=0,hor=H,1)

X<-E$inp

Y<-E$out

N<-NROW(X)

Y.cont<-KNN.acf(X,Y,rev(TS.tr[(N.tr-H):N.tr]),TS=TS.tr)

plot(t[(N-H+1):N],TS.ts)

lines(t[(N-H+1):N],Y.cont)

}

\references{

\emph{Bontempi G. Ben Taieb S. Conditionally dependent strategies for multiple-step-ahead prediction in local learning, International Journal of Forecasting Volume 27, Issue 3, July–September 2011, Pages 689–699}

}

\author{

Gianluca Bontempi \email{gbonte@ulb.ac.be}

}