Refresh Workspace or SAProcessing

Usage

.jsap_refresh(
  jsap,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed"),
  period = 0,
  start = NULL,
  end = NULL,
  info = c("All", "Data", "None")
)

.jws_refresh(
  jws,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed"),
  period = 0,
  start = NULL,
  end = NULL,
  info = c("All", "Data", "None")
)

Arguments

policy: the refresh policy to apply (see details).
period, start, end: to specify the span on which outliers will not be re-identified (i.e.: re-detected) when policy = "Outliers" or policy = "Outliers_StochasticComponent". Span definition: period: numeric, number of observations in a year (12, 4...). start and end: first and last date from which outliers will not be re-identfied, defined as arrays of two elements: year and first period (for example, if period = 12, c(1980, 1) for January 1980). If they are not specified, the outliers will be re-identified on the whole series.
info: information to refresh.
jws, jsap: Java Workspace or Multiprocessing

Details

Available refresh policies are:

Current: applying the current pre-adjustment reg-arima model and adding the new raw data points as Additive Outliers (defined as new intervention variables)

Fixed: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points.

FixedParameters: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders and coefficients are unchanged.

FixedAutoRegressiveParameters: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed.

FreeParameters: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed.

Outliers: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated

Outliers_StochasticComponent: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified.