AnalyzeTS (version 2.2)

Gfuzzy.ts2: Updating

Description

Updating

Usage

Gfuzzy.ts2(ts, n = 7, w = 7, D1 = 0, D2 = 0, C = list(C1 = NULL, C2 = NULL), 
forecast = 5, plot = FALSE, grid = FALSE, type = "Abbasov-Mamedova")

Arguments

ts

Univariate time series.

n

A numeric vector where each element is number of fuzzy set.

w

A numeric vector where each element is w parameter.

D1, D2

Two proper positive numbers.

C

A list consiting 2 component C1 and C2 or a rusult object from GDOC function.

forecast

Number of points to forecast in future.

plot

Let plot=TRUE to paint graph of obsevation series and fuzzy series. Let plot=FLASE (default) to do not paint graph.

grid

If TRUE, a gray background grid is put on the graph.

type

Model is choosed to predicts time series by fuzziness, type = "Abbasov-Manedova" or type = "NFTS" or both.

Value

A list with three component. A list with three component.

Details

Gfuzzy.ts2 function consider length(n)*length(w)*length(type) models combining from three parameter n, w and type, and then using fuzzy.ts2 function analyze for each submodel.

References

Abbasov, A.M. and Mamedova, M.H., 2003. Application of fuzzy time series to population forecasting, Proceedings of 8th Symposion on Information Technology in Urban and Spatial Planning, Vienna University of Technology, February 25-March1, 545-552.

See Also

Using Gfuzzy.ts1 function in case only a fuzzy time series model.

Examples

#data(enrollment)
#g.C<-GDOC(enrollment,n=c(5,7,9),w=c(7,9),D1=0,D2=0,
#CEF="MSE",type=c("Abbasov-Mamedova","NFTS"))
#g.fuzzy1<-Gfuzzy.ts2(enrollment,n=c(5,7,9),w=c(7,9),D1=0,D2=0,C=g.C,forecast=5, 
#plot=1,grid=0,type=c("Abbasov-Mamedova","NFTS"))