TD_HOLT_WINTERS_FORECASTER

The Holt-Winters forecasting approach, aka, 'Triple Exponential Smoothing'; is one of a number of exponential smoothing approaches to forecasting that can be used to predict data points in a series, provided that that series contains 'seasonal' aspects, meaning that the series is repetitive over some 'period'. . This forecasting approach was born out of research aimed at forecasting trends in production, inventories and labor force. The idea behind triple exponential smoothing is to apply exponential smoothing to the seasonal components in addition to also applying the exponential smoothing to the level and trend components. The smoothing is applied across seasons, e.g. the seasonal component of the 3rd point into the season would be exponentially smoothed with the one from the 3rd point of last season, 3rd point two seasons ago, etc.

Input ports

1. Type: DB Session
   Teradata Connection
   Connection to a Teradata Database Instance
2. Type: Table
   Input
   Passed in a logical univariate series, this function will forecast 'h' periods ('h' steps) into the future, meaning 'h' periods beyond the last observed sample point for the passed in series. This function outputs up to four result sets. The primary result set contains the historical data points, the forecasted data points, and the prediction level data points. This result set can be retrieved by issuing a SELECT against the analytical result table. The optional secondary result set contains a collection of goodness-of-fit metrics, roughly equivalent to those returned by the independent TD_FIT_METRICS function. The optional tertiary result set contains a collection of model selection / validation metrics, roughly equivalent to those returned by the TD_SELECTION_CRITERIA function. The optional quaternary result set contains the residual series, associated with the fitting and forecasting operation.

Output ports

1. Type: Table
   output of TD_HOLT_WINTERS_FORECASTER
   output of TD_HOLT_WINTERS_FORECASTER