Predicts the values of a univariate time series based on exponential smoothing.


icon

Inputs/Outputs

  • ci32.png number of points —

    number of points specifies the length of the predicted time series. The default is 1.

  • c1ddbl.png Xt —

    Xt specifies the univariate time series.

  • cu16.png exponential type —

    exponential type specifies the type of exponential smoothing scheme to use in the prediction.

  • cnclst.png exponential factors —

    exponential factors specifies the weighting factors for exponential smoothing.

  • cdbl.png level —

    level specifies the weight for the level cumulant. The value of value must be a number between 0 and 1.

  • cdbl.png trend —

    trend specifies the weight for the trend cumulant. The value of trend must be a number between 0 and 1. This option is available only when exponential type is Double or Triple.

  • cdbl.png season —

    season specifies the weight for the seasonal cumulant. The value of season must be a number between 0 and 1. This option is available only when exponential type is Triple.

  • cerrcodeclst.png error in (no error) —

    error in describes error conditions that occur before this node runs. This input provides standard error in functionality.

  • ci32.png season period —

    season period specifies the length of the seasonal pattern in the input time series. The default is 1. This option is available only when exponential type is Triple.

  • cu16.png season type —

    season type specifies the way in which this VI models the seasonality. This option is available only when exponential type is Triple.

  • i1ddbl.png predicted series —

    predicted series returns the predicted univariate time series.

  • ierrcodeclst.png error out —

    error out contains error information. This output provides standard error out functionality.

    • TSA Exponential Prediction Details

    This VI computes the future values of a time series based on one of the following exponential smoothing schemes: single, double, and triple (Holt-Winters). You can specify the type of exponential smoothing scheme using the exponential type parameter. Each exponential smoothing scheme has a corresponding forecasting formula that uses the computed level cumulant, trend cumulant, and season cumulant vector.

    Examples

    Refer to the Exponential Prediction VI in the labview\examples\Time Series Analysis\TSAGettingStarted directory for an example of using the TSA Exponential Prediction VI.