fuzzy.ts2: Abbasov Mamedova model

Description

Predicts time series by fuzziness method according to Abbasov-Manedova model.

Usage

fuzzy.ts2(ts, n = 5, w = NULL, D1 = 0, D2 = 0, C = NULL, trace = FALSE,
forecast = NULL, plot = FALSE, fty = c("ts", "f"))

Arguments

Observation series.

Number of fuzzy set.

The 'w' parameter.

A adequate value.

A optional constant.

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.

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.

fty

fty="f", N(length(ts)+1) = N(length(ts)) + V(length(ts)+1).

fty="ts", N(length(ts)+1) = ts(length(ts)) + V(length(ts)+1).

Value

type: Name of fuzzy model.
table1: Information about changing fuzzy sets.
table2: Observation series and changing series.
table3: The change fuzzy of observation series.
table4: Interpolate values.
table5: Forecasting values.
table6: The change fuzzy of forecasting series.
timeseries: Forecasting timeseries.
accuracy: Information about 7 accuracy of forecasting model.

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.

Examples

Run this code

data(population)
layout(1:2)
fuzzy.ts2(population,n=7,w=7,C=0.0001,forecast=11,fty="ts",trace=TRUE,plot=TRUE)
fuzzy.ts2(population,n=5,w=5,C=0.01,forecast=5,fty="f",plot=TRUE)