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Exponential smoothing prediction builds an exponential smoothing model for a time series and then predicts the future values of the time series based on the model. Exponential smoothing prediction is suitable for a time series that contains trends and seasonal variations.
Use the TSA Exponential Prediction VI to predict the future values of univariate or multivariate time series based on exponential smoothing.
The following figure shows an example of performing exponential smoothing prediction on a simulated sales record.
The Sales Record graph contains a univariate time series of a sales record for a certain product. This example splits the time series into two parts—one part for modeling and the other part for comparison—and specifies the following settings for prediction:
The Prediction Result graph in the previous figure shows the Predicted Record plot matches the Real Record plot.
Refer to the Exponential Prediction VI in the labview\examples\Time Series Analysis\TSAGettingStarted directory for an example that demonstrates how to forecast the future values of a univariate time series based on the exponential smoothing.