HANA R-Triple Exponential Smoothing

Properties that can be configured for the HANA R-Triple Exponential Smoothing algorithm.

Syntax Use this algorithm to smooth the source data and find seasonal trends in data.
HANA R-Triple Exponential Smoothing Properties
Table 1: Algorithm Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm.
  • Trend: Displays source data along with predicted values for the given dataset.
  • Forecast: Displays forecasted values for the given time period.
Target Variable Select the target column for which you want to perform time series analysis.
Period Select the period for forecasting.
Periods Per Year Select the period for forecasting. This option is only enabled if you select "Custom" for "Period".
Start Year Enter the year from which the observations must be considered. For example, 2009, 1987, 2019.
Start Period Enter the period from which the observations must be considered.
Periods to Predict Enter the number of periods to forecast. This value is used only if the output mode is Forecast.
Predicted Column Name Enter a name for the newly created column that contains the predicted values.
Year Values Enter a name for the newly created column that contains year values.
Quarter Values Enter a name for the newly created column that contains quarter values.
Month Values Enter a name for the newly created column that contains month values.
Period Values Enter a name for the newly created column that contains period values.
Alpha Enter a smoothing constant for smoothing observations (base parameters). Range: 0-1.
Beta Enter a smoothing constant for finding trend parameters. Range: 0-1.
Gamma Enter a smoothing constant for finding seasonal trend parameters. Range: 0-1.
Seasonal Select the type of HoltWinters Exponential Smoothing algorithm.
Confidence Level Enter the confidence level of the algorithm.
No. Periodic Observations Enter the number of periodic observations required to start the calculation.
Level Enter the start value for level (a[0]) (l.start). For example: 0.4.
Trend Enter the start value for finding trend parameters (b[0]) (b.start). For example: 0.4.
Season Enter start values for finding seasonal parameters (s.start). This value is dependent on the column you select. For example, if you select quarter as period, you need to provide four double values.
Optimizer Inputs Enter the starting values for alpha, beta, and gamma required for the optimizer. For example: 0.3, 0.1, 0.1.