local.trend: Calculate local trends using cumsum

Description

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

Usage

local.trend(x, k=mean(x), plotit=TRUE, type="l", cols=1:2, ltys=2:1,
    xlab="Time", ylab="cusum", ...)
# S3 method for local.trend
identify(x, ...)

Value

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

Arguments

x: a regular time series (a 'ts' object) for local.trend() or a 'local.trend' object for identify()
k: the reference value to substract from cumulated sums. By default, it is the mean of all observations in the series
plotit: if plotit=TRUE (by default), a graph with the cumsum curve superposed to the original series is plotted
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

Author

Frédéric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean (phgrosjean@sciviews.org)

Details

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

References

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.

Examples

Run this code

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...

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