Calculates the prediction interval(s) for additive forecast based on the historical data using ETS or EDS algorithms. EDS is used when argument period_length is 0, otherwise ETS is used.

「指数平滑」是一种在时间序列中平滑实际值, 以便预测可能的未来值的方法。

「指数三重平滑」(ETS) 是一组处理趋势和周期性 (季节性) 影响的算法。「指数双重平滑」(EDS) 是一种类似 ETS 的算法, 但没有周期性的影响。EDS 生成线性预测。

forecast = basevalue + trend * ∆x + periodical_aberration。

### 语法

FORECAST.ETS.PI.ADD(target, values, timeline, [confidence_level], [period_length], [data_completion], [aggregation])

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

The timeline does not have to be sorted, the functions will sort it for calculations.
The timeline values must have a consistent step between them.
If a constant step cannot be identified in the sorted timeline, the functions will return the #NUM! error.
If the ranges of both the timeline and the historical values are not the same size, the functions will return the #N/A error.
If the timeline contains fewer than 2 data periods, the functions will return the #VALUE! error.

1

AVERAGE

2

COUNT

3

COUNTA

4

MAX

5

MEDIAN

6

MIN

7

SUM

#### 示例

 A B 01/2013 112 02/2013 118 03/2013 132 04/2013 100 05/2013 121 06/2013 135 07/2013 148 08/2013 148 09/2013 136 10/2013 119 11/2013 104 12/2013 118