Description Usage Arguments Value Author(s) References See Also Examples
SeasonalTrend decomposition of time series data using Regression.
1 2 3 
data 
Time series or a vector of length L. 
predictors 
List of predictors.

strDesign 
An optional parameter used to create the design matrix. It is used internally in the library to improve performance when the design matrix does not require full recalculation. 
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. 
confidence 
A vector of percentiles giving the coverage of confidence intervals.
It must be greater than 0 and less than 1.
If 
solver 
A vector with two string values. The only supported combinations are: c("Matrix", "cholesky") (default), and c("Matrix", "qr"). The parameter is used to specify a particular library and method to solve the minimisation problem during STR decompositon. 
reportDimensionsOnly 
A boolean parameter. When TRUE the method constructs the design matrix and reports its dimensions without proceeding further. It is mostly used for debugging. 
trace 
When 
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.
method – always contains string "STRmodel"
for this function.
Alexander Dokumentov
Dokumentov, A., and Hyndman, R.J. (2016) STR: A SeasonalTrend Decomposition Procedure Based on Regression www.monash.edu/business/econometricsandbusinessstatistics/research/publications/ebs/wp1315.pdf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  n < 50
trendSeasonalStructure < list(segments = list(c(0,1)), sKnots = list(c(1,0)))
ns < 5
seasonalStructure < list(segments = list(c(0,ns)), sKnots = c(as.list(1:(ns1)),list(c(ns,0))))
seasons < (0:(n1))%%ns + 1
trendSeasons < rep(1, length(seasons))
times < seq_along(seasons)
data < seasons + times/4
plot(times, data, type = "l")
timeKnots < times
trendData < rep(1, n)
seasonData < rep(1, n)
trend < list(data = trendData, times = times, seasons = trendSeasons,
timeKnots = timeKnots, seasonalStructure = trendSeasonalStructure, lambdas = c(1,0,0))
season < list(data = seasonData, times = times, seasons = seasons,
timeKnots = timeKnots, seasonalStructure = seasonalStructure, lambdas = c(10,0,0))
predictors < list(trend, season)
str1 < STRmodel(data, predictors)
plot(str1)
data[c(3,4,7,20,24,29,35,37,45)] < NA
plot(times, data, type = "l")
str2 < STRmodel(data, predictors)
plot(str2)

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