Bin point valuesin Chen-Hsu model

Description

Calculating bin point values, which devece divide fuzzy sets in Chen-Hsu model.

Usage

ChenHsu.bin(table, n.subset)

Arguments

Value

A vector contain bin point values.

Author(s)

Hong Viet Minh <hongvietminh@gmail.com>

Examples

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)

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> 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/ChenHsu.bin.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ChenHsu.bin
> ### Title: Bin point valuesin Chen-Hsu model
> ### Aliases: ChenHsu.bin
> ### Keywords: ChenHsu.bin
> 
> ### ** Examples
> 
> 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

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>