Automatically selects parameters for an STR decomposition of time series data.
The time series should be of class ts
or msts
.
Usage
AutoSTR(
data,
robust = FALSE,
gapCV = NULL,
lambdas = NULL,
reltol = 0.001,
confidence = NULL,
nsKnots = NULL,
trace = FALSE
)
Arguments
- data
A time series of class
ts
ormsts
.- robust
When
TRUE
, Robust STR decomposition is used. Default isFALSE
.- gapCV
An optional parameter defining the length of the sequence of skipped values in the cross validation procedure.
- lambdas
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.
- reltol
An optional parameter which is passed directly to
optim()
when optimising the parameters of the model.- confidence
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.- nsKnots
An optional vector parameter, defining the number of seasonal knots (per period) for each sesonal component.
- trace
When
TRUE
, tracing is turned on.
Value
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.
References
Dokumentov, A., and Hyndman, R.J. (2022) STR: Seasonal-Trend decomposition using Regression, INFORMS Journal on Data Science, 1(1), 50-62. https://robjhyndman.com/publications/str/