Automatically selects parameters for an STR decomposition of time series data.
The time series should be of class ts
or msts
.
AutoSTR(data, robust = FALSE, gapCV = NULL, lambdas = NULL,
reltol = 0.001, confidence = NULL, nsKnots = NULL, trace = FALSE)
A time series of class ts
or msts
.
When TRUE
, Robust STR decomposition is used. Default is FALSE
.
An optional parameter defining the length of the sequence of skipped values in the cross validation procedure.
An optional parameter. A structure which replaces lambda parameters provided with predictors. It is used as either a starting point for the optimisation of parameters or as the exact model parameters.
An optional parameter which is passed directly to optim()
when optimising the parameters of the model.
A vector of percentiles giving the coverage of confidence intervals.
It must be greater than 0 and less than 1.
If NULL
, no confidence intervals are produced.
An optional vector parameter, defining the number of seasonal knots (per period) for each sesonal component.
When TRUE
, tracing is turned on.
A structure containing input and output data.
It is an S3 class STR
, which is a list with the following components:
output -- contains decomposed data. It is a list of three components:
predictors -- a list of components where each component corresponds to the input predictor. Every such component is a list containing the following:
data -- fit/forecast for the corresponding predictor (trend, seasonal component, flexible or seasonal predictor).
beta -- beta coefficients of the fit of the coresponding predictor.
lower -- optional (if requested) matrix of lower bounds of confidence intervals.
upper -- optional (if requested) matrix of upper bounds of confidence intervals.
random -- a list with one component data, which contains residuals of the model fit.
forecast -- a list with two components:
data -- fit/forecast for the model.
beta -- beta coefficients of the fit.
lower -- optional (if requested) matrix of lower bounds of confidence intervals.
upper -- optional (if requested) matrix of upper bounds of confidence intervals.
input -- input parameters and lambdas used for final calculations.
data -- input data.
predictors - input predictors.
lambdas -- smoothing parameters used for final calculations (same as input lambdas for STR method).
cvMSE -- optional cross validated (leave one out) Mean Squared Error.
optim.CV.MSE -- best cross validated Mean Squared Error (n-fold) achieved during minimisation procedure.
nFold -- the input nFold
parameter.
gapCV -- the input gapCV
parameter.
method -- always contains string "AutoSTR"
for this function.
Dokumentov, A., and Hyndman, R.J. (2016) STR: A Seasonal-Trend Decomposition Procedure Based on Regression www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp13-15.pdf
# NOT RUN {
# Decomposition of a multiple seasonal time series
decomp <- AutoSTR(calls)
plot(decomp)
# Decomposition of a monthly time series
decomp <- AutoSTR(log(grocery))
plot(decomp)
# }
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