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 |

`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 |

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
#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")
``` |

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