Seasonal forecasting methods extend the nonseasonal forecasting methods by adding an additional component to capture the seasonal behavior of the data. Predictor offers the listed classic seasonal forecasting methods.

For associated parameters, see Classic Seasonal Forecasting Method Parameters.

Seasonal Additive Method

Calculates a seasonal index for historical data that does not have a trend. The method produces exponentially smoothed values for the level of the forecast and the seasonal adjustment to the forecast. The seasonal adjustment is added to the forecasted level, producing the seasonal additive forecast.

This method is best for data without trend but with seasonality that does not increase over time. It results in a curved forecast that reproduces the seasonal changes in the data.

Figure 70. Typical Seasonal Additive Data, Fit, and Forecast Curve without Trend

Cyclical graph of single additive historical and forecasted data

Seasonal Multiplicative Method

Calculates a seasonal index for historical data that does not have a trend. The method produces exponentially smoothed values for the level of the forecast and the seasonal adjustment to the forecast. The seasonal adjustment is multiplied by the forecasted level, producing the seasonal multiplicative forecast.

This method is best for data without trend but with seasonality that increases or decreases over time. It results in a curved forecast that reproduces the seasonal changes in the data.

Figure 71. Typical Seasonal Multiplicative Data, Fit, and Forecast Curve without Trend

Upward trending cyclical graph of seasonal multiplicative historical and forecasted data

Holt-Winters’ Additive Seasonal Method

Is an extension of Holt's exponential smoothing that captures seasonality. The method produces exponentially smoothed values for the level of the forecast, the trend of the forecast, and the seasonal adjustment to the forecast. This seasonal additive method adds the seasonality factor to the trended forecast, producing the Holt-Winters’ additive forecast.

This method is best for data with trend and seasonality that does not increase over time. It results in a curved forecast that shows the seasonal changes in the data.

Figure 72. Typical Holt-Winters’ Additive Data, Fit, and Forecast Curve

Upward trending cyclical graph of Holt-Winters' additive historical and forecasted data

Holt-Winters’ Multiplicative Seasonal Method

Is similar to the Holt-Winters’ additive method. Holt-Winters’ Multiplicative method also calculates exponentially smoothed values for level, trend, and seasonal adjustment to the forecast. This seasonal multiplicative method multiplies the trended forecast by the seasonality, producing the Holt-Winters’ multiplicative forecast.

This method is best for data with trend and with seasonality that increases over time. It results in a curved forecast that reproduces the seasonal changes in the data.

Figure 73. Typical Holt-Winters’ Multiplicative Data, Fit, and Forecast Curve

Upward trending, amplitude increasing, cyclical graph of Holt-Winters' multiplicative historical and forecasted data

Damped Trend Additive Seasonal Method

Separates a data series into seasonality, damped trend, and level; projects each forward; and reassembles them into a forecast in an additive manner.

This method is best for data with a trend and with seasonality. It results in a curved forecast that flattens over time and reporoduces the seasonal cycles.

Figure 74. Typical Damped Trend Additive Data, Fit, and Forecast Curve

Upward trending, cyclical graph of damped trend additive historical and forecasted data

Damped Trend Multiplicative Seasonal Method

Separates a data series into seasonality, damped trend, and level; projects each forward; and reassembles them into a forecast in a multiplicative manner.

This method is best for data with a trend and with seasonality. It results in a curved forecast that flattens over time and reporoduces the seasonal cycles.

Figure 75. Typical Damped Trend Multiplicative Data, Fit, and Forecast Curve

Upward trending, amplitude increasing, cyclical graph of damped trend multiplicative historical and forecasted data

Classic Seasonal Forecasting Method Parameters

The seasonal forecast methods use three smoothing parameters: alpha, beta, and gamma:

  • alpha (α) — Smoothing parameter for the level component of the forecast. The value of alpha can be any number between 0 and 1, not inclusive.

  • beta (β) — Smoothing parameter for the trend component of the forecast. The value of beta can be any number between 0 and 1, not inclusive.

  • gamma (γ) — Smoothing parameter for the seasonality component of the forecast. The value of gamma can be any number between 0 and 1, not inclusive.

  • phi (Φ) — Damping parameter; any number between 0 and 1, not inclusive.

Each seasonal forecasting method uses some or all of these parameters, depending on the forecasting method. For example, the seasonal additive forecasting method does not account for trend, so it does not use the beta parameter. The damped trend methods use phi in addition to the other three.