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.
FORECAST.ETS.PI.ADD calculates with the model
FORECAST.ETS.PI.ADD(target, values, timeline, [confidence_level], [period_length], [data_completion], [aggregation])
For example, with a 90% Confidence level, a 90% prediction interval will be computed (90% of future points are to fall within this radius from forecast).
Note on prediction intervals: there is no exact mathematical way to calculate this for forecasts, there are various approximations. Prediction intervals tend to be increasingly 'over-optimistic' when increasing distance of the forecast-X from the observation data set.
For ETS, Calc uses an approximation based on 1000 calculations with random variations within the standard deviation of the observation data set (the historical values).
Returns 18.8061295551355, the prediction interval for additive forecast for January 2014 based on Values and Timeline named ranges above, 90% (=0.9) confidence level, with one sample per period, no missing data, and AVERAGE as aggregation.
Returns 23.4416821953741, the prediction interval for additive forecast for January 2014 based on Values and Timeline named ranges above, with confidence level of 0.8, period length of 4, no missing data, and SUM as aggregation.