Type: Package
Title: Weighted Ensemble for Hybrid Model
Version: 0.1.0
Author: Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut, cre]
Maintainer: Dr. Md Yeasin <yeasin.iasri@gmail.com>
Description: The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.
License: GPL-3
Encoding: UTF-8
Imports: stats, metaheuristicOpt
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2023-04-07 17:41:25 UTC; YEASIN
Repository: CRAN
Date/Publication: 2023-04-10 14:10:06 UTC

Weighted Ensemble for Hybrid Model

Description

Weighted Ensemble for Hybrid Model

Usage

WeightedEnsemble(df, Method = "PSO", test_data = NULL, forecast = NULL)

Arguments

df

Data set (training result) with first column as observed value

Method

Method of optimization

test_data

Test result

forecast

Forecast result

Value

References

J. S. Armstrong. Combining forecasts: The end of the beginning or the beginning of the end? International Journal of Forecasting, 5(4):585–588, 1989.

Examples


y1<-rnorm(100,mean=100,sd=50)
y2<- rnorm(100,mean=100,sd=50)
y3<- rnorm(100,mean=100,sd=50)
y4<-rnorm(100,mean=100,sd=50)
y<-rnorm(100,mean=100,sd=50)
data<-cbind(y,y1,y2,y3,y4)
OptiSemble<-WeightedEnsemble(df=data)