Description Usage Arguments Details Value See Also Examples

Function `ucm`

decomposes a time series into components such as trend, seasonal, cycle, and the regression effects due to predictor series using Unobserved Components Model (UCM).

1 2 3 4 |

`formula` |
an object of class |

`data` |
a required data frame or list containing variables in the model. |

`irregular` |
logical; if irregular component is to be included in the model. Defaults to |

`irregular.var` |
value to fix variance of irregular component. |

`level` |
logical; if level is to be included in the model. Defaults to |

`level.var` |
value to fix variance of level component. |

`slope` |
logical; if slope is to be included in the model along with level. Defaults to |

`slope.var` |
value to fix variance of the slope component. |

`season` |
logical; if seasonal component is to be included in the model. Defaults to |

`season.length` |
value of length of seasonal component. Required when |

`season.var` |
value to fix variance of seasonal component. |

`cycle` |
logical; if cyclical component is to be included in the model. Defaults to |

`cycle.period` |
length of cyclical component. Required when |

`cycle.var` |
value to fix variance of cyclical component. |

Formula of the model can be of the forma as in `lm`

with response variable on rhs and predictor variables or 0 (if no predictor variables) on the rhs.

object of class `ucm`

, which is a list with the following components:

`est` |
Estimates of predictor variables, if present. |

`irr.var` |
Estimated variance of irregular component, if present. |

`est.var.level` |
Estimated variance of the level component, if present. |

`est.var.slope` |
Estimated variance of slope of the level, if present. |

`est.var.season` |
Estimated variance of the seasonal component, if present. |

`est.var.cycle` |
Estimated variance of the cyclical component, if present. |

`s.level` |
An object of the same class as of dependent variable containing the time varying level values, if level is present. |

`s.lope` |
An object of the same class as of dependent variable containing the time varying slope values, if slope is present. |

`s.season` |
An object of the same class as of dependent variable containing the time varying seasonal values, if season is present. |

`s.cycle` |
An object of the same class as of dependent variable containing the time varying cyclical values, if cycle is present. |

`vs.level` |
A vector containing time varying estimated variance of level, if level is present. |

`vs.slope` |
A vector containing time varying estimated variance of slope, if slope is present. |

`vs.season` |
A vector containing time varying estimated variance of seasonal component, if season is present. |

`vs.cycle` |
A vector containing time varying estimated variance of cyclical component, if cycle is present. |

`call` |
Original call of the function. |

`model` |
The original model of class |

`KFAS`

, `SSModel`

for a detailed discussion on State Space Models.

1 2 3 |

```
Loading required package: KFAS
Call:
ucm(formula = Nile ~ 0, data = Nile, slope = TRUE)
Parameter estimates:
NULL
Estimated variance:
Irregular_Variance Level_Variance Slope_Variance
14706.3488 1742.8776 0.0037
Time Series:
Start = 1871
End = 1970
Frequency = 1
[1] 1120.8009 1117.4788 1109.1175 1118.0728 1116.1336 1108.9957 1095.8132
[8] 1116.1474 1122.9887 1100.5562 1073.4490 1055.6391 1052.1264 1041.7550
[15] 1037.0432 1034.3513 1040.4710 1030.0549 1047.0217 1074.5386 1094.2975
[22] 1113.3805 1121.0129 1125.2097 1114.6172 1086.7950 1043.1863 1001.1402
[29] 947.3782 914.1638 889.7388 867.1794 865.1441 854.2378 845.8487
[36] 854.6263 856.1307 877.0868 881.1062 865.1100 836.8018 809.1814
[43] 791.4195 813.4091 834.1440 870.5398 877.3717 857.8198 841.3280
[50] 834.0008 828.2145 829.5646 829.0858 824.4694 815.4054 820.2556
[57] 822.1736 833.3563 848.9664 841.9368 844.7365 855.0898 864.2687
[64] 875.7314 879.1034 870.0440 857.7901 849.7778 822.7773 801.9132
[71] 795.9715 807.4479 814.3556 821.5425 838.1563 859.1736 858.7608
[78] 858.2011 855.7691 854.2579 848.5107 855.1494 874.3680 897.8965
[85] 903.3988 907.1706 901.6000 908.4256 913.5237 911.3360 920.5651
[92] 918.0096 916.8773 917.6264 888.4659 856.5162 837.6638 809.1721
[99] 791.4852 782.9813
```

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