Decomposition of seasonal time series data using stlm from forecast package. This function is used internally in ts.analysis.
The input univariate seasonal time series data
If TRUE the results are returned in json format, default returns a list
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.
A list with the following components:
trend: The estimated trend component
seasonal: The estimated seasonal component
remainder: The estimated remainder component
time: The time of the series was sampled
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: The residuals of the model (fitted innovations)
fitted: The model's fitted values
time the time of tsdata
line The y=0 line
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.