Description Usage Arguments Details Value Note Author(s) References See Also Examples

A simple method using cumulated sums that allows to detect changes in the tendency in a time series

1 2 3 4 |

`x` |
a regular time series (a 'ts' object) for |

`k` |
the reference value to substract from cumulated sums. By default, it is the mean of all observations in the series |

`plotit` |
if |

`type` |
the type of plot (as usual notation for this argument) |

`cols` |
colors to use for original data and for the trend line |

`ltys` |
line types to use for original data and the trend line |

`xlab` |
label of the x-axis |

`ylab` |
label of the y-axis |

`...` |
additional arguments for the graph |

With `local.trend()`

, you can:

- detect changes in the mean value of a time series

- determine the date of occurence of such changes

- estimate the mean values on homogeneous intervals

a 'local.trend' object is returned. It has the method `identify()`

Once transitions are identified with this method, you can use
`stat.slide()`

to get more detailed information on each phase. A
smoothing of the series using running medians (see `decmedian()`

) allows
also to detect various levels in a time series, but according to the median
statistic. Under **R**, see also the 'strucchange' package for a more complete,
but more complex, implementation of cumsum applied to time series.

Frédéric Ibanez ([email protected]), Philippe Grosjean ([email protected])

Ibanez, F., J.M. Fromentin & J. Castel, 1993. *Application de la méthode
des sommes cumulées à l'analyse des séries chronologiques océanographiques.*
C. R. Acad. Sci. Paris, Life Sciences, 316:745-748.

`trend.test`

, `stat.slide`

,
`decmedian`

1 2 3 4 5 6 | ```
data(bnr)
# Calculate and plot cumsum for the 8th series
bnr8.lt <- local.trend(bnr[,8])
# To identify local trends, use:
# identify(bnr8.lt)
# and click points between which you want to compute local linear trends...
``` |

```
Loading required package: boot
```

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