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Statistical Excel 2016 ◉ 46 views

=FORECAST.ETS.CONFINT()

Returns a confidence interval for the forecast value at the specified target date.

Applies To: Excel for Microsoft 365Excel for Microsoft 365 for MacExcel 2024Excel 2024 for MacExcel 2021Excel 2021 for MacExcel 2019Excel 2016
=FORECAST.ETS.CONFINT(target_date, values, timeline, [confidence_level], [seasonality], [data_completion], [aggregation])
Hover argument names in the table for details.
Output typeNumber
Popularity10 / 100
VolatileNo
Dynamic ArrayNo
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◧ Arguments

ArgumentTypeRequiredDescription
target_date Date Required The data point for which you want to predict a value. Target date can be date/time or numeric. If the target date is chronologically before the end of the historical timeline, FORECAST.ETS.CONFINT returns the #NUM! error.
values Range Required Values are the historical values, for which you want to forecast the next points.
timeline Range Required The independent array or range of numeric data. The dates in the timeline must have a consistent step between them and can't be zero. The timeline isn't required to be sorted, as FORECAST.ETS.CONFINT will sort it implicitly for calculations. If a constant step can't be identified in the provided timeline, FORECAST.ETS.CONFINT will return the #NUM! error. If timeline contains duplicate values, FORECAST.ETS.CONFINT will return the #VALUE! error. If the ranges of the timeline and values aren't of same size, FORECAST.ETS.CONFINT will return the #N/A error.
confidence_level Number Optional A numerical value between 0 and 1 (exclusive), indicating a confidence level for the calculated confidence interval. For example, for a 90% confidence interval, a 90% confidence level will be computed (90% of future points are to fall within this radius from prediction). The default value is 95%. For numbers outside of the range (0,1), FORECAST.ETS.CONFINT will return the #NUM! error.
seasonality Number Optional A numeric value. The default value of 1 means Excel detects seasonality automatically for the forecast and uses positive, whole numbers for the length of the seasonal pattern. 0 indicates no seasonality, meaning the prediction will be linear. Positive whole numbers will indicate to the algorithm to use patterns of this length as the seasonality. For any other value, FORECAST.ETS.CONFINT will return the #NUM! error. Maximum supported seasonality is 8,760 (number of hours in a year). Any seasonality above that number will result in the #NUM! error.
data Number Optional completion Optional. Although the timeline requires a constant step between data points, FORECAST.ETS.CONFINT supports up to 30% missing data, and will automatically adjust for it. 0 will indicate the algorithm to account for missing points as zeros. The default value of 1 will account for missing points by completing them to be the average of the neighboring points.
aggregation Number Optional Although the timeline requires a constant step between data points, FORECAST.ETS.CONFINT will aggregate multiple points which have the same time stamp. The aggregation parameter is a numeric value indicating which method will be used to aggregate several values with the same time stamp. The default value of 0 will use AVERAGE, while other options are SUM, COUNT, COUNTA, MIN, MAX, MEDIAN.

▣ Examples

Example 1 =FORECAST.ETS.CONFINT(A38, B2:B37, A2:A37)
Example 2 =FORECAST.ETS(A38, B2:B37, A2:A37) + FORECAST.ETS.CONFINT(A38, B2:B37, A2:A37, 0.9)

✦ Tips

  • The function FORECAST.ETS.CONFINT is not available in Excel for the Web, iOS, or Android.

⚠ Common Errors

#NUM! If the target date is chronologically before the end of the historical timeline, FORECAST.ETS.CONFINT returns the #NUM! error. If a constant step can't be identified in the provided timeline, FORECAST.ETS.CONFINT will return the #NUM! error. For numbers outside of the range (0,1), FORECAST.ETS.CONFINT will return the #NUM! error. For any other value, FORECAST.ETS.CONFINT will return the #NUM! error. Any seasonality above that number will result in the #NUM! error.
Fix: تاریخ هدف را بعد از خط زمانی تنظیم کنید، گام ثابت را بررسی کنید، confidence_level را بین ۰ تا ۱ وارد کنید و seasonality مجاز بگذارید.
#VALUE! If timeline contains duplicate values, FORECAST.ETS.CONFINT will return the #VALUE! error.
Fix: مقادیر تکراری را از خط زمانی حذف کنید.
#N/A If the ranges of the timeline and values aren't of same size, FORECAST.ETS.CONFINT will return the #N/A error.
Fix: Make the two ranges the same size

⇄ Related Functions

=FORECAST.ETS() Calculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm. The predicted value is a continuation of the historical values in the specified target date, which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements, or consumer trends. This function requires the timeline to be organized with a constant step between the different points. =CONFIDENCE.NORM() The confidence interval is a range of values. Your sample mean, x, is at the center of this range and the range is x ± CONFIDENCE.NORM. For example, if x is the sample mean of delivery times for products ordered through the mail, x ± CONFIDENCE.NORM is a range of population means. For any population mean, μ0, in this range, the probability of obtaining a sample mean further from μ0 than x is greater than alpha; for any population mean, μ0, not in this range, the probability of obtaining a sample mean further from μ0 than x is less than alpha. In other words, assume that we use x, standard_dev, and size to construct a two-tailed test at significance level alpha of the hypothesis that the population mean is μ0. Then we will not reject that hypothesis if μ0 is in the confidence interval and will reject that hypothesis if μ0 is not in the confidence interval. The confidence interval does not allow us to infer that there is probability 1 – alpha that our next package will take a delivery time that is in the confidence interval. =FORECAST.ETS.STAT() Returns a statistical value as a result of time series forecasting.

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