Automatic Exponential Smoothing Method

The primary goal for a demand forecasting solution is automation. The Automatic Exponential Smoothing method (Auto ES) automatically selects an exponential smoothing model to a time series. Auto ES is a proprietary forecasting algorithm that compares the varying ES methods against each other for the best fit while being the least complex.

Auto ES considers a collection of six candidate models:
  1. Simple Exponential Smoothing (Simple ES):

    • Simple ES ignores seasonality and trend features in the demand data.

    • Simple ES is effective when there are short time horizons in which less than one year of historic demand data is available or data is untrended and unseasonal.

    • Simple ES produces a flat line (Level only) forecast.

  2. Holt Exponential Smoothing (Holt ES):

    • Holt ES captures trends but is non-seasonal.

    • Holt ES treats data as a one-dimensional trend (up or down).

    • Holt ES provides forecast estimates by combining an estimated trend for the forecast horizon and the smoothed level at the end of the series.

    • Holt ES is effective for short or long horizons and provides a damping effect for long horizons.

  3. Additive Winters Exponential Smoothing (Additive Winters ES):

    • Additive Winters ES extracts seasonal indexes assumed to have additive effects on the de-seasonalized series. Effects are applied additively to levels and trends.

    • Additive Winters ES requires sufficient data of at least two years of history.

    • Additive Winters ES controls the smoothing of the components of level, trend, and seasonality.

    • Additive Winters ES is effective when there are zeros in the history.

  4. Multiplicative Winters Exponential Smoothing (Multiplicative Winters ES):

    • Multiplicative Winters ES extracts seasonal indexes assumed to have multiplicative effects on the de-seasonalized series. Effects are applied multiplicatively to levels and trends.

    • Multiplicative Winters ES requires sufficient data of at least two years of history.

    • Multiplicative Winters ES controls the smoothing of the components of level, trend, and seasonality.

    • Multiplicative Winters ES does not work when there are zeros in the history.

  5. Seasonal Regression:

    • Seasonal Regression is for sales forecasts based entirely on sales from the same time period last year. Forecasting using only last year sales involves simple calculations and often outperforms more sophisticated seasonal models. –

    • Seasonal Regression performs best when dealing with highly seasonal sales data with a short sales history.

    • Seasonal regression is designed to address retailer needs when sales history is between 56 to 104 weeks.

    • Seasonal Regression uses simple linear regression with last year’s sales as the predictor variable and this year’s sales as the target variable.

    • If there are significant shifts from year to year, Seasonal Regression will learn the shift and appropriately weigh last year’s data, keeping the same shape but adjusting the scale.

  6. Bayesian:

    • The Bayesian method combines historic sales data with sales plan data and is effective for new products with little or no historic data. The sales plan provides expert knowledge. The sales plan defines the shape of the selling profile or lifecycle and the scale of the total quantity expected to be sold over the plan's duration. The Bayesian method merges a customer’s sales plans with any available historical sales.

    • The Bayesian method starts with a seasonal sales plan and evaluates the following conditions to calculate the forecast:

      • When no sales history exists, the forecast equals the sales plan.

      • As sales data becomes available, the forecast adjusts, and the scale becomes a weighted average reflecting known history.

      • Once sufficient sales data is collected, it can be switched to one of the different standard forecasting methods.

During configuration, an implementer may determine if only Seasonal Exponential Smoothing (Seasonal ES) methodologies are appropriate and remove access to Simple ES and Holt ES methods.