# Documentation/Calc Functions/FORECAST.ETS.STAT.ADD

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## Function name:

FORECAST.ETS.STAT.ADD

## Category:

Statistical Analysis

## Summary:

Returns statistical value(s) that are results of the ETS/EDS algorithms.

## Syntax:

FORECAST.ETS.STAT.ADD (**values**, **timeline**, **stat_type**, [**period_length**], [**data_completion**], [**aggregation**])

## Returns:

## Arguments:

**values** (mandatory): A numeric array or range. **values** are the historical values, for which you want to forecast the next points.

**timeline** (mandatory): A numeric array or range. The time line (x-value) range for the historical values.

The time line doesn't have to to be sorted, the functions will sort it for calculations. The time line values must have a consistent step between them. If a constant step can't be identified in the sorted time line, the functions will return the #NUM! error. If the ranges of the time line and historical values aren't of same size, the functions will return the #N/A error. If the time line contains less than 2 periods of data, the functions will return the #VALUE! Error.

**stat_type** (mandatory): A numerical value from 1 to 9. A value indicating which statistic will be returned for the given values and x-range.

The following statistics can be returned:

stat_type | Statistics |
---|---|

1 | Alpha smoothing parameter of ETS algorithm (base) |

2 | Gamma smoothing parameter of ETS algorithm (trend) |

3 | Beta smoothing parameter of ETS algorithm (periodic deviation) |

4 | Mean absolute scaled error (MASE) - a measure of the accuracy of forecasts. |

5 | Symmetric mean absolute percentage error (SMAPE) - an accuracy measure based on percentage errors. |

6 | Mean absolute error (MAE) – a measure of the accuracy of forecasts. |

7 | Root mean squared error (RMSE) - a measure of the differences between predicted and observed values. |

8 | Step size detected time line (x-range). When a stepsize in months/quarters/years is detected, the stepsize is in months, otherwise the stepsize is in days in case of date(time) timeline and numeric in other cases. |

9 | Number of samples in period – this is the same as argument period_length, or the calculated number in case of argument period_length being 1. |

**period_length** (optional): A numeric value >= 0, the default is 1. A positive integer indicating the number of samples in a period.

A value of 1 indicates that Calc is to determine the number of samples in a period automatically. A value of 0 indicates no periodic effects, a forecast is calculated with EDS algorithms. For all other positive values, forecasts are calculated with ETS algorithms. For values that not being a positive whole number, the functions will return the #NUM! Error.

**data_completion** (optional): a logical value TRUE or FALSE, a numeric 1 or 0, default is 1 (TRUE). A value of 0 (FALSE) will add missing data points with zero as historical value. A value of 1 (TRUE) will add missing data points by interpolating between the neighboring data points.

Although the time line requires a constant step between data points, the function support up to 30% missing data points, and will add these data points.

**aggregation** (optional): A numeric value from 1 to 7, with default 1. The aggregation parameter indicates which method will be used to aggregate identical time values:

Aggregation | Function |
---|---|

1 | AVERAGE |

2 | COUNT |

3 | COUNTA |

4 | MAX |

5 | MEDIAN |

6 | MIN |

7 | SUM |

Although the time line requires a constant step between data points, the functions will aggregate multiple points which have the same time stamp.

## Additional details:

Exponential Smoothing is a method to smooth real values in time series in order to forecast probable future values.

Exponential Triple Smoothing (ETS) is a set of algorithms in which both trend and periodical (seasonal) influences are processed. Exponential Double Smoothing (EDS) is an algorithm like ETS, but without the periodical influences. EDS produces linear forecasts.

See the Wikipedia on Exponential smoothing algorithms for more information.

FORECAST.ETS.STAT.ADD calculates with the model

forecast = basevalue + trend * ∆x + periodical_aberration.

## Examples:

The table below contains a timeline and its associated values:

A | B | |
---|---|---|

1 | Timeline | Values |

2 | 01/2013 | 112 |

3 | 02/2013 | 118 |

4 | 03/2013 | 132 |

5 | 04/2013 | 100 |

6 | 05/2013 | 121 |

7 | 06/2013 | 135 |

8 | 07/2013 | 148 |

9 | 08/2013 | 148 |

10 | 09/2013 | 136 |

11 | 10/2013 | 119 |

12 | 11/2013 | 104 |

13 | 12/2013 | 118 |

=FORECAST.ETS.STAT.ADD(Values;Timeline;3;1;TRUE();1)

Returns 0.9990234375, the additive statistics based on **Values** and **Timeline** named ranges above, with beta smoothing, one sample per period, no missing data, and AVERAGE as aggregation.

=FORECAST.ETS.STAT.ADD(Values;Timeline;2;1;TRUE();7)

Returns 0.0615234375, the additive statistics based on **Values** and **Timeline** named ranges above, with gamma smoothing, no missing data, and SUM as aggregation.

## Related LibreOffice functions:

FORECAST

FORECAST.ETS.ADD

FORECAST.ETS.MULT

FORECAST.ETS.PI.ADD

FORECAST.ETS.PI.MULT

FORECAST.ETS.SEASONALITY

FORECAST.ETS.STAT.MULT

FORECAST.LINEAR