Syntax
GROUP_HOLT_WINTERS(<number>;<number>;<number>;[<number>])
values: series values
α: data smoothing factor (between 0.0 and 1.0)
β: trend smoothing factor (between 0.0 and 1.0)
h: prediction range (how far the future should be predicted) - optional, default = 1
Description
This function computes Holt-Winters double exponential smoothing (non-seasonal) on a time series. Smoothing a time series helps remove random noise and leave the user with a general trend. Whereas in the simple moving average (GROUP_MOVING_AVERAGE) the past observations are weighted equally, exponential smoothing assigns exponentially decreasing weights over time. This gives a stronger weight to more recent values and can lead to better predictions.
This function can't be used referring to the same sheet, so make sure to use a different sheet than the value source sheet.
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How to set alpha (data smoothing) and beta (trend smoothing). Both can be set with values between 0.0 and 1.0) Alpha - Set a larger data smoothing value to reduce a greater amount of noise from the data. Use caution as setting the data smoothing value too high when your data doesn't have much noise can reduce data quality. |
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In the following example we have price values per month over a two year period. Let's try to predict the first 5 months of the third year using double exponential smoothing.
- Add five additional rows in the date column to better visualize the results for 5 months of future predictions.
- Create a new worksheet in your workbook by duplicating the source sheet.
- Create a group key using GROUPBY(1).
- Sort the timeline using GROUP_SORT_ASC(#Monthly_Sales!Date).
- Click an empty column to bring up the Fx button on the formula line to display the formula builder (As of of Datameer v67.4, click the Fx button on the formula line to display the formula builder2, the formula builder is located in the worksheet inspector) and select GROUP_HOLT_WINTERS
. - Use the monthly sales column for the value's argument.
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