Download Help (Windows Only) |

**Owning Palette:** Modeling and Prediction VIs

**Requires:** Advanced Signal Processing Toolkit

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

Wire data to the **Xt** input to determine the polymorphic instance to use or manually select the instance.

Use the pull-down menu to select an instance of this VI.

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

Xt specifies the univariate time series. | |||||||

exponential type specifies the type of exponential smoothing scheme to use in the prediction. Refer to the Details section for more information about each exponential type.
| |||||||

exponential factors specifies the weighting factors for exponential smoothing.
| |||||||

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

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. | |||||||

season type specifies the way in which this VI models the seasonality. This option is available only when exponential type is Triple. Refer to the Details section for information about each season type.
| |||||||

predicted series returns the predicted univariate time series. | |||||||

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

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

Xt specifies the univariate time series. | |||||||

exponential type specifies the type of exponential smoothing scheme to use in the prediction. Refer to the Details section for more information about each exponential type.
| |||||||

exponential factors specifies the weighting factors for exponential smoothing.
| |||||||

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

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. | |||||||

season type specifies the way in which this VI models the seasonality. This option is available only when exponential type is Triple. Refer to the Details section for information about each season type.
| |||||||

predicted series returns the predicted univariate time series. | |||||||

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

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.

In the single exponential smoothing scheme, this VI obtains the level cumulant *X _{a}*. The corresponding

*X _{t}*(

where *N* is the number of original time series *X _{t}*.

In the double exponential smoothing scheme, this VI obtains the level cumulant *X _{a}* and trend cumulant

*X _{t}*(

Refer to the TSA Exponential Average VI for the descriptions of the level cumulant *X _{a}* and trend cumulant

In the triple exponential smoothing scheme, this VI obtains the level cumulant *X _{a}*, the trend cumulant

*X _{t}*(

If the **season type** is set as **Additive**, the corresponding *k*-step-ahead prediction at time *t* is defined according to the following equation:

*X _{t}*(

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.

Helpful

Not Helpful