fuzzy.ts2: Abbasov Mamedova model and FVD model
In AnalyzeTS: Analyze Fuzzy Time Series

Description Usage Arguments Value Author(s) References See Also Examples

Calculating fuzziness and forecast time series by fuzziness method according to Abbasov - Manedova (2010) and NFTS models.

1 2	fuzzy.ts2(ts, n = 7, w = 7, D1 = 0, D2 = 0, C = NULL, forecast = 5, r = 12, trace = FALSE, plot = FALSE, grid = FALSE, type = "Abbasov-Mamedova")

`ts`	Univariate time series.
`n`	Number of fuzzy set.
`w`	The w parameter.
`D1, D2`	Two proper positive numbers.
`C`	A optional constant.
`forecast`	Number of points to forecast in future.
`r`	Display results returned to the specified number of decimal places (default 12). (See `round2str` for details of r paramicter.)
`trace`	Let trace=TRUE to print all of calculation results out to creen. Let trace=FALSE (default) to only print forecasting series out to creen.
`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".

When trace = TRUE, results are returned as a list containing the following components.

`type`	The value of type.
`table1`	Information about changing fuzzy sets consit four column: set is name of the fuzzy sets, low and up are upper and lower bounds of the fuzzy sets, and mid is middle values corresponding every fuzzy set.
`table2`	Series - observation consit three column: point is time of observation, ts is the original series, and diff.ts is changing series from original series.
`table3`	The change fuzzy of original series.
`table4`	Series - interpolation consit three column: point is time of interpolation, interpolate is the series - interpolation, and diff.interpolate is changing series from series - interpolation.
`table5`	Forecasting series consit three column: point is time of forecast, forecast is the forecasting series, and diff.forecast is changing series from forecasting series.
`table6`	The change fuzzy of forecasting series.
`accuracy`	Information about the criterion to evaluate forecasting model.

When trace = FALSE, results are returned as a list containing two components.

`interpolate`	Series - interpolation.
`forecast`	Forecasting series.

Doan Hai Nghi <Hainghi1426262609121094@gmail.com>

Tran Thi Ngoc Han <tranthingochan01011994@gmail.com>

Hong Viet Minh <hongvietminh@gmail.com>

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.

Gfuzzy.ts2

#data(enrollment)
#layout(1:2)
#NF.mod<-fuzzy.ts2(enrollment,n=7,w=7,C=0.0001,forecast=11,trace=TRUE,plot=TRUE,type="NFTS")
#AM.mod<-fuzzy.ts2(enrollment,n=5,w=5,C=0.01,forecast=5,plot=TRUE,type="Abbasov-Mamedova")
#NF.mod
#AM.mod

#Finding the best C value by DOC function
#Abbasov-Mamedova model
#str.C1<-DOC(enrollment,n=7,w=7,D1=0,D2=0,CEF="MAPE",type="Abbasov-Mamedova")
#C1<-as.numeric(str.C1[1])
#fuzzy.ts2(enrollment,n=7,w=7,D1=0,D2=0,C=C1,forecast=5,type="Abbasov-Mamedova")

#NFTS model
#str.C2<-DOC(enrollment,n=7,w=7,D1=0,D2=0,CEF="MAPE",type="NFTS")
#C2<-as.numeric(str.C2[1])
#fuzzy.ts2(enrollment,n=7,w=7,D1=0,D2=0,C=C1,forecast=5,type="NFTS")