Description Usage Arguments Details Value Author(s) References See Also

View source: R/ts.seasonal.decomp.R

Decomposition of seasonal time series data using stlm from forecast package. This function is used internally in ts.analysis.

1 | ```
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 |

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:

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

Kleanthis Koupidis

add

okgreece/TimeSeries.OBeu documentation built on June 17, 2018, 7:51 a.m.

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