deseasonalizeTSpdf: Deseasonlize a time series using harmonic model fitting and...

In jreiche/bayts: Probabalistic approach to combine multiple time series and Bayesian probability updating for near real-time forest change detection

Description

Method as described in Reiche et al., 2018 (Remote Sensing)

Usage

deseasonalizeTSpdf(tsL = list(NULL, ...), distNFL = list(), msdL = list(),
  start, formula = response ~ trend + harmon, order = 1,
  residuals = FALSE)

Arguments

tsL	list of object(s) of class ts.
msdL	list of msdl object(s) describing the modulation of the sd of F and NF sd(F),sd(NF),mean(NF) (e.g. 2,2,-4)
start	Start date of monitoring period. Default=NULL (start of input time series).
formula	formula for the regression model. The default is response ~ trend + harmon, i.e., a linear trend and a harmonic season component. Other specifications are possible using all terms set up by bfastpp, i.e., season (seasonal pattern with dummy variables), lag (autoregressive terms), slag (seasonal autoregressive terms), or xreg (further covariates). See bfastpp for details.
order	numeric. Order of the harmonic term, defaulting to 3.
residuals	TRUE = output are time series of deseasonalised "residuals"; FALSE = time series of deseasonalised values
pdfL	list of "pdf" object(s) describing F and NF distributions (see calcPNF).
distL	list of "distNF" object(s) describing the mean and sd of the NF distribution in case no data driven way to derive the NF distribution is wanted; default=NULL

Value

deseasonlized time series (residuals or real values) and Gaussian pdfs of defining F and NF distribution

Author(s)

Johannes Reiche (Wageningen University)

jreiche/bayts documentation built on Feb. 3, 2021, 1:12 a.m.