timeshift: Time lag estimation via cross-correlation of two timeseries

In marcianito/UmbrellaEffect: Gravity data reduction procedure (related to the shielding of its building)

Description

This function is based on standard time series decomposition and afterwards cross-correlation estimation between the two input datasets.

The main aim is to find dominant time shifts between two time series (e.g. soil moisture, climate data, precipitation stations, etc).

Usage

timeshift(data1, data2, nmax, norm = T, stl = F, plotting = T, swin, twin,
  sensor1 = "data1", sensor2 = "data2", ...)

Arguments

data1, data2	Timeseries of type .zoo.
nmax	Number of correlation values output.
norm	logical. Use normalized data (default) or not.
stl	logical. Use decomposed data for cross-correlation (default) or not.
plotting	logical. Output cross-correlation plots. Default is F, own output plots are provided. Should be kept FALSE.
swin	Time window used for seasonality estimation. Only used if stl=T.
twin	Time window used for trend estimation. Only used if stl=T.
...	Further parameters passed to internal functions.

Details

missing

References

Marvin Reich (2014), [e-mail]

Examples

outside.cor = ccf.zoo(besidesBuilding$mux43_04,besidesBuilding$mux43_08,5,T,T,T,24,17521)

marcianito/UmbrellaEffect documentation built on July 1, 2019, 8:30 p.m.