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FORECAST.ETS.STAT

This function return a statistical value as a result of time series forecasting. Statistic type indicates which statistic is requested by this function.

Syntax

=FORECAST.ETS.STAT(values,timeline,statistic_type,seasonality,[data_completion],[aggregation])

Arguments

The function has the following arguments:

Argument Description
values [Required] is a range of the historical values for which you want to predict a new point.
timeline [Required] is a range of date/time values that correspond to the historical values. The timeline range must be of the same size as the values range. Date/time values must have a constant step between them.
statistic_type [Required] A numeric value between 1 and 8, indicating which statistic will be returned for the calculated forecast. The table below shows the eight possible statistical values and their corresponding results.
seasonality [Optional] is a numeric value that specifies which method should be used to detect the seasonality. The possible values are listed below.
data_completion [Optional] is a numeric value that specifies how to process the missing data points in the timeline data range. The possible values are:
1 or omitted: Missing points are calculated as the average of the neighboring points.
0: Missing points are treated as zero values.
aggregation [Optional] is a numeric value that specifies which function should be used to aggregate identical time values in the timeline data range. The possible values are listed below.

Statistic types

Value Statistic type Description
1 Alpha It is the base parameter of ETS algorithm. Higher values indicate more weight to recent data.
2 Beta It is the trend parameter of ETS algorithm. Higher values indicate more weight to recent trends.
3 Gamma It is the seasonality parameter of ETS algorithm. Higher values indicate more weight to recent seasonal periods.
4 MASE It stands for Mean Absolute Scaled Error metric, a measure of forecast accuracy.
5 SMAPE It stands for Symmetric Mean Absolute Percentage Error metric, an accuracy measure based on percentage errors.
6 MAE It stands for the Mean Absolute percentage Error metric, a measure of accuracy based on percentage errors.
7 RMSE It stands for Root Mean Squared Error metric, a measure of differences between predicted and observed values.
8 Step Size It is detected in the historical data timeline.

Seasonality

  • 1 or omitted: Seasonality is detected automatically. Positive, whole numbers are used for the length of the seasonal pattern.
  • 0: No seasonality, the prediction will be linear.
  • An integer greater than or equal to 2: The specified number is used for the length of the seasonal pattern.

Aggregation values

Value Description
1 or omitted AVERAGE
2 COUNT
3 COUNTA
4 MAX
5 MEDIAN
6 MIN
7 SUM

Data Types

Returns a specified statistical value relating to a time series.

Examples

Version Available

This function is available in product version 16.0 or later.