local.trend {pastecs}R Documentation

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, ...)
## S3 method for class 'local.trend':
identify(loctrd)

Arguments

x a regular time series (a 'rts' object under S+ or a 'ts' object under R)
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
... additional arguments for the graph
loctrd a 'local.trend' object, as returned by the function local.trend()

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

Value

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

Note

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.

Author(s)

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

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.

See Also

trend.test, stat.slide, decmedian

Examples

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...

[Package pastecs version 1.3-8 Index]