Holt Winters

The Holt-Winters functions produces a smoothed value for time series based on the range in the time series, accounting for seasonal adjustments. Implements the Holt-Winters method of exponentially-weighted averages as smoothing factors.

sf

The smoothing factor, where lower values weigh towards the importance of older data.

Valid values for this scalar parameter must be between 0 and 1.

tf

The trend factor, where higher values weigh in favor of trends in the time series.

Valid values for this scalar parameter must be between 0 and 1.

In Dashboards

To use Holt-Winters in a dashboard, apply the following function:

holt_winters( \
  ${promql} \ (1)
  ${time_period}, \ (2)
  0.1, \
  0.9 \
)
code
1 ${promql}: PromQL query to evaluate
2 ${time_period}: The time frame for generating a forecast.

Limitations

Holt-Winters was initially developed in 1957 by a Charles Holt (Holt), and later extended by his student Peter Winters in 1960 (Winters) to introduce a trend component to represent seasonality. The method has performed well in many situations, but it was originally developed for a specific type of data — trend-seasonal. The original method also implies that noise has an additive form, while the seasonality is multiplicative.

Many advancements in forecast modeling took place in the intervening years, including the development of the ETS framework in 2008 (Hyndman et al.), which covers 30 possible models for time series with different types of error, trend, and seasonal components. The Holt-Winters Seasonal method aligns with only one of the models in the framework.

For an in-depth discussion, see Why you should not use Holt-Winters method by Ivan Svetunkov.

Next steps