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Last data update: 2014.03.03

R: Chen, Sing, Heuristic and Chen-Hsu models

fuzzy.ts1

R Documentation

Chen, Sing, Heuristic and Chen-Hsu models

Description

Calculates fuzziness of time series with Chen, Singh, Heuristic and Chen-Hsu.

Usage

fuzzy.ts1(ts, n = 5, D1 = 0, D2 = 0, type = c("Chen", "Singh","Heuristic",
"Chen-Hsu"), bin = NULL, trace = FALSE, plot = FALSE)

Arguments

`ts`	Observation series.
`n`	Number of fuzzy set.
`D1`	A adequate value.
`D2`	A adequate value.
`type`	Type of model.
`bin`	Point values use to divide fuzzy stes for Chen-Hsu model. If bin=NULL (default) then function just inform information about fuzzy sets.
`trace`	Let trace=TRUE to print all of calculation results out to creen. Let trace=FALSE (default) to only print fuzzy 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.

Value

`type`	Name of fuzzy model.
`table1`	Information about fuzzy sets.
`table2`	Information about fuzzy series of Chen, Sing, Heuristic and Chen-Hsu models (in bin!=NUL).
`accuracy`	Information about 7 accuracy of forecasting model.

Author(s)

Doan Hai Nghi <Hainghi1426262609121094@gmail.com>

Tran Thi Ngoc Han <tranthingochan01011994@gmail.com>

Hong Viet Minh <hongvietminh@gmail.com>

References

Chen, S.M., 1996. Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems. 81: 311-319.

Chen, S.M. and Hsu, C.C., 2004. A New method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering, 12: 234-244.

Huarng, H., 2001. Huarng models of fuzzy time series for forecasting. Fuzzy Sets and Systems. 123: 369-386.

Singh, S.R., 2008. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation. 79: 539-554

Examples

par(mfrow=c(2,2))
chen10<-fuzzy.ts1(lh,n=5,type="Chen",plot=TRUE)
singh10<-fuzzy.ts1(lh,n=5,type="Singh",plot=TRUE)
heuristic10<-fuzzy.ts1(lh,n=5,type="Heuristic",plot=TRUE)

#useing ChenHsu.bin function to find divide point fuzzy set values.
a<-fuzzy.ts1(lh,type="Chen-Hsu",plot=1)
b<-ChenHsu.bin(a$table1,n.subset=c(1,2,1,1,1))
fuzzy.ts1(lh,type="Chen-Hsu",bin=b,plot=1,trace=1)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AnalyzeTS)
Loading required package: MASS
Loading required package: TSA
Loading required package: leaps
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
Loading required package: tseries

Attaching package: 'TSA'

The following objects are masked from 'package:stats':

    acf, arima

The following object is masked from 'package:utils':

    tar

Loading required package: TTR
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AnalyzeTS/fuzzy.ts1.Rd_%03d_medium.png", width=480, height=480)
> ### Name: fuzzy.ts1
> ### Title: Chen, Sing, Heuristic and Chen-Hsu models
> ### Aliases: fuzzy.ts1
> ### Keywords: fuzzy.ts1
> 
> ### ** Examples
> 
> par(mfrow=c(2,2))
> chen10<-fuzzy.ts1(lh,n=5,type="Chen",plot=TRUE)
> singh10<-fuzzy.ts1(lh,n=5,type="Singh",plot=TRUE)
> heuristic10<-fuzzy.ts1(lh,n=5,type="Heuristic",plot=TRUE)
> 
> #useing ChenHsu.bin function to find divide point fuzzy set values.
> a<-fuzzy.ts1(lh,type="Chen-Hsu",plot=1)
> b<-ChenHsu.bin(a$table1,n.subset=c(1,2,1,1,1))
> fuzzy.ts1(lh,type="Chen-Hsu",bin=b,plot=1,trace=1)
$type
[1] "Chen-Hsu"

$table1
  set  dow   up   mid num
1  A1 1.40 1.82 1.610   8
2  A2 1.82 2.03 1.925   6
3  A3 2.03 2.24 2.135   7
4  A4 2.24 2.66 2.450  11
5  A5 2.66 3.08 2.870   9
6  A6 3.08 3.50 3.290   7

$table2
   point  ts relative forecast
1      1 2.4  A4-x-NA       NA
2      2 2.4  A4<--A4   2.4500
3      3 2.4  A4<--A4   2.3450
4      4 2.2  A3<--A4   2.1350
5      5 2.1  A3<--A3   2.0825
6      6 1.5  A1<--A3   1.6100
7      7 2.3  A4<--A1   2.5550
8      8 2.3  A4<--A4   2.4500
9      9 2.5  A4<--A4   2.4500
10    10 2.0  A2<--A4   1.9250
11    11 1.9  A2<--A2   1.9250
12    12 1.7  A1<--A2   1.5050
13    13 2.2  A3<--A1   2.1350
14    14 1.8  A1<--A3   1.6100
15    15 3.2  A6<--A1   3.2900
16    16 3.2  A6<--A6   3.2900
17    17 2.7  A5<--A6   2.8700
18    18 2.2  A3<--A5   2.1350
19    19 2.2  A3<--A3   2.1875
20    20 1.9  A2<--A3   1.8725
21    21 1.9  A2<--A2   1.9250
22    22 1.8  A1<--A2   1.5050
23    23 2.7  A5<--A1   2.8700
24    24 3.0  A5<--A5   2.8700
25    25 2.3  A4<--A5   2.4500
26    26 2.0  A2<--A4   1.9250
27    27 2.0  A2<--A2   1.9250
28    28 2.9  A5<--A2   2.8700
29    29 2.9  A5<--A5   2.8700
30    30 2.7  A5<--A5   2.8700
31    31 2.7  A5<--A5   2.7650
32    32 2.3  A4<--A5   2.5550
33    33 2.6  A4<--A4   2.3450
34    34 2.4  A4<--A4   2.3450
35    35 1.8  A1<--A4   1.6100
36    36 1.7  A1<--A1   1.5050
37    37 1.5  A1<--A1   1.5050
38    38 1.4  A1<--A1   1.5050
39    39 2.1  A3<--A1   2.1350
40    40 3.3  A6<--A3   3.2900
41    41 3.5  A6<--A6   3.2900
42    42 3.5  A6<--A6   3.2900
43    43 3.1  A6<--A6   3.1850
44    44 2.6  A4<--A6   2.5550
45    45 2.1  A3<--A4   2.1350
46    46 3.4  A6<--A3   3.2900
47    47 3.0  A5<--A6   2.8700
48    48 2.9  A5<--A5   2.8700

$accuracy
            ME   MAE   MPE  MAPE   MSE RMSE     U
Chen.Hsu 0.018 0.105 0.867 4.616 0.017 0.13 0.259

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> dev.off()
null device 
          1 
>