KNN.pls: KNN.pls

In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"

KNN.pls

Description

Multioutput KNN

Arguments

X:	training input [N,n]
Y:	training output [N,m]
X.ts:	test input [N.ts,n]
k:	min number of neighbours
dist:	type of distance: euclidean, cosine
F:	forgetting factor
C:	integer parameter which sets the maximum number of neighbours (Ck)
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
Acf:	autocorrelation function of the training series

Details

KNN.pls

Multioutput KNN for multi-step-ahed prediction. It performs a locally constant model with weighted combination of local model on the basis of partial least-squares error

Value

vector of N.ts predictions

Author(s)

Gianluca Bontempi [e-mail]

References

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

Examples

## Multi-step ahead time series forecasting
library(pls)
require(gbcode)
t=seq(0,400,by=0.1)
N<-length(t)
H<-1500 ## 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))
n=3
E=MakeEmbedded(TS.tr,n=n,delay=0,hor=H,1)
X<-E$inp
Y<-E$out
ACF.lag<-5
Y.cont<-KNN.pls(X,Y,rev(TS.tr[(N.tr-n+1):N.tr]))
plot(t[(N-H+1):N],TS.ts)
lines(t[(N-H+1):N],Y.cont,col="red")


rm(list=ls())
require(gbcode)
data(A)
N<-NROW(A)
H<-200 ## horizon prediction
TS<-A[,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))
n=16
E=MakeEmbedded(TS.tr,n=16,delay=0,hor=H,1)
X<-E$inp
Y<-E$out
Y.cont<-KNN.pls(X,Y,rev(TS.tr[(N.tr-n+1):N.tr]))
plot((N-H+1):N,TS.ts)
lines((N-H+1):N,Y.cont)

gbonte/gbcode documentation built on Feb. 27, 2024, 7:38 a.m.