Decomposition of seasonal time series data using stlm from forecast package. This function is used internally in ts.analysis.
ts.seasonal.decomp(tsdata, tojson=F)
| tsdata | The input univariate seasonal time series data |
|---|---|
| tojson | If TRUE the results are returned in json format, default returns a list |
A list with the following components:
stl.plot:
trend: The estimated trend component
seasonal: The estimated seasonal component
remainder: The estimated remainder component
time: The time of the series was sampled
stl.general:
model.summary The summary object of the arima model to use in forecast if needed
stl.win: An integer vector of length 3 indicating the spans used for the "s", "t", and "l" smoothers
stl.degree: An integer vector of length 3 indicating the polynomial degrees for these smoothers
residuals_fitted:
residuals: The residuals of the model (fitted innovations)
fitted: The model's fitted values
time the time of tsdata
line The y=0 line
compare:
arima.order: The Arima order
arima.coef: A vector of AR, MA and regression coefficients
arima.coef.se: The standard error of the coefficients
covariance.coef: The matrix of the estimated variance of the coefficients
resid.variance: The MLE of the innovations variance
not.used.obs: The number of not used observations for the fitting
used.obs: the number of used observations for the fitting
loglik: The maximized log-likelihood (of the differenced data), or the approximation to it used
aic: The AIC value corresponding to the log-likelihood
bic: The BIC value corresponding to the log-likelihood
aicc: The second-order Akaike Information Criterion corresponding to the log-likelihood
Decomposition of seasonal time series data through arima models is based on stlm from forecast package and returns a list with useful parameters for OBEU.
add
ts.analysis, stlm