Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.

# FORECAST Function in Excel (with other Forecasting Functions)

In Excel, there are different forecasting functions to predict future results based on existing values. In this article, we will show you how to use the FORECAST function with other forecasting functions in Excel.

## What is FORECAST Function in Excel?

• Description

The FORECAST function is a Statistical function in Excel. It calculates or predicts a future value based on existing value. The existing values are known as x-values and y-values and the future value is predicted by using linear regression. For instance, you can predict future numeric values of sales, earnings and expenses, inventory, consumer trends, measurements etc.

• Purpose

To predict or calculate a future value with a linear trend

• Syntax

=FORECAST(x, known_ys, known_xs)

• Arguments Description
Value Required/Optional Description
x Required The value for which the future value to predict or calculate
known_ys Required The dependent array or range of data (y values)
known_xs Required The independent array or range of data (x values)
• Return Value

A predicted or calculated value

## Examples of the FORECAST with Other Forecasting Functions in Excel

In this section, you will learn the FORECAST.LINEAR, the FORECAST.ETS, the FORECAST.ETS.CONFINT, the FORECAST.ETS.SEASONALITY and the FORECAST.ETS.STAT function in Excel.

### 1. FORECAST.LINEAR Function in Excel

FORECAST.LINEAR is formerly known as the FORECAST function in Excel. Microsoft replaced the FORECAST function with the FORECAST.LINEAR in 2016.

• Purpose

This function predicts the future value based on the existing set of values.

• Equation

`y = a + bx`

Where,

a = constant value, intercept, which follows, And b = coefficient, the slope of the line, which follows, Here, means, the Average value (arithmetic mean) of the sample value.

• Return Value

A calculated future value Based on the above discussion, the FORECAST.LINEAR formula for our given dataset will be,

`=FORECAST.LINEAR(B18,\$C\$5:\$C\$16,\$B\$5:\$B\$16)`

Where,

B18 = The value for which the future value to predict or calculate

\$C\$5:\$C\$16 = The dependent array or range of data (y values)

\$B\$5:\$B\$16 = The independent array or range of data (x values)

### 2. FORECAST.ETS in Excel

The FORECAST.ETS function is used to calculate or predict future value based on existing values by using the AAA version of the Exponential Smoothing (ETS) algorithm.

Here,

ETS = Exponential Triple Smoothing algorithm.

This algorithm loosens up the insignificant deviations in data trends by detecting seasonality patterns and confidence intervals.

• Syntax

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

• Argument Description
Value Required/Optional Description
target_date Required The timeline for the prediction should be calculated
values Required Existing or historical value(y-values), dependent array or range of data from which a prediction will be calculated.
timeline Required Numeric independent array or range of values (x-values)
[seasonality] Optional

Seasonality calculation.

• 1 = Default. Automatic seasonality for the forecast
• 0 = No seasonality, linear prediction
• n = length of the season in timeline units
• #NUM! error = For any other value in FORECAST.ETS
[data_completion] Optional

Missing data calculation.

• 1 = Default. Completes the calculation to the nearest point of the average value.
• 0 = Takes as zero.
• FORECAST.ETS supports 30% of missing data.
[aggregation] Optional Indicates which method to use. Default value is 0 = Average
• Return Value

A calculation of predicted value Based on the discussion, the FORECAST.ETS formula for our given dataset shown above will be,

`=FORECAST.LINEAR(B18,\$C\$5:\$C\$16,\$B\$5:\$B\$16)`

Where,

B18 = The value for which the future value to predict or calculate

\$C\$5:\$C\$16 = The dependent array or range of data (y values)

\$B\$5:\$B\$16 = The independent array or range of data (x values)

### 3. FORECAST.ETS.CONFINT

The FORECAST.ETS.CONFINT function returns a confidence interval (CI) for a forecast value at a specified timeline. A confidence level of 90% means the predicted values are expected to fall within this radius from the result that the FORECAST.ETS function produced.

• Syntax

=FORECAST.ETS.CONFINT (target_date, values, timeline, [confidence_level], [seasonality], [data_completion], [aggregation])

• Argument Description
Value Required/Optional Description
target_date Required The timeline for the prediction should be calculated
values Required Existing or historical value(y-values), dependent array or range of data from which a prediction will be calculated.
timeline Required Numeric independent array or range of values (x-values)
[confidence_level] Optional The confidence level for the calculated confidence interval. A numeric value between 0 and 1 (exclusive). Default 0.95 or 95%
[seasonality] Optional Seasonality calculation.
• 1 = Default. Automatic seasonality for the forecast
• 0 = No seasonality, linear prediction
• n = length of the season in timeline units
• #NUM! error = For any other value in FORECAST.ETS
[data_completion] Optional Missing data calculation.
• 1 = Default. Completes the calculation to the nearest point of the average value.
• 0 = Takes as zero.
• FORECAST.ETS supports 30% of missing data.
[aggregation] Optional Indicates which method to use. Default value is 0 = Average
• Return Value

Confidence Interval (CI) value Based on the above discussion, the FORECAST.ETS.CONFINT formula for our given dataset will be,

`=FORECAST.ETS.CONFINT(E5,\$C\$5:\$C\$16,\$B\$5:\$B\$16,G5)`

Where,

E5 = The value for which the future value to predict or calculate

\$C\$5:\$C\$16 = The dependent array or range of data (y values)

\$B\$5:\$B\$16 = The independent array or range of data (x values)

G5 = Confidence level

Read More: Time Series Forecasting Methods in Excel

### 4. FORECAST.ETS.SEASONALITY

The FORECAST.ETS.SEASONALITY function is used to return the length of a repetitive pattern in a specified timeline.

• Syntax

=FORECAST.ETS.SEASONALITY (values, timeline, [data_completion], [aggregation])

• Argument Description
Value Required/Optional Description
values Required Existing or historical value(y-values), dependent array or range of data from which a prediction will be calculated.
timeline Required Numeric independent array or range of values (x-values)
[data_completion] Optional Missing data calculation.
• 1 = Default. Completes the calculation to the nearest point of the average value.
• 0 = Takes as zero.
• FORECAST.ETS supports 30% of missing data.
[aggregation] Optional Indicates which method to use. Default value is 0 = Average
• Return Value

Season length in a specified timeline Based on the above discussion, the FORECAST.ETS.SEASONALITY formula for our given dataset shown above will be,

`=FORECAST.ETS.SEASONALITY(\$C\$5:\$C\$16,\$B\$5:\$B\$16)`

Where,

\$C\$5:\$C\$16 = The dependent array or range of data (Score column as y values)

\$B\$5:\$B\$16 = The independent array or range of data (ID column as x values)

### 5. FORECAST.ETS.STAT in Excel

The FORECAST.ETS.STAT function returns a statistical value relating to the time series forecasting with the FORECAST.ETS function.

Syntax

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

• Argument Description
Value Required/Optional Description
values Required Existing or historical value(y-values), dependent array or range of data from which a prediction will be calculated.
timeline Required Numeric independent array or range of values (x-values)
statistic_type Required The type of statistical value to return. The table below shows the 8 possible types and their description,
• 1 = Alpha = Returns the base parameter of the ETS algorithm. Higher values give more weight to recent data.
• 2 = Beta = Returns the trend parameter of the ETS algorithm. Higher values give more weight to recent trends.
• 3 = Gamma = Returns the seasonality parameter of the ETS algorithm. Higher values give more weight to recent seasonal periods.
• 4 = MASE = Returns the mean absolute scaled error metric. A measure of forecast accuracy.
• 5 = SMAPE = Returns the symmetric absolute percentage error metric. A measure of accuracy based on percentage errors.
• 6 = MAE = Returns the symmetric absolute percentage error metric. A measure of accuracy based on percentage errors.
• 7 = RMSE = Returns the root mean squared error metric. A measure of the differences between predicted and observed values.
• 8 = Step Size = Returns the step size detected in the historical data timeline.
[seasonality] Optional Seasonality calculation.
• 1 = Default. Automatic seasonality for the forecast
• 0 = No seasonality, linear prediction
• n = length of the season in timeline units
• #NUM! error = For any other value in FORECAST.ETS
[data_completion] Optional Missing data calculation.
• 1 = Default. Completes the calculation to the nearest point of the average value.
• 0 = Takes as zero.
• FORECAST.ETS supports 30% of missing data.
[aggregation] Optional Indicates which method to use. Default value is 0 = Average
• Return Value

A statistical result

The formula for the FORECAST.ETS.STAT function with different statistic types is shown in the picture below, The formula,

`=FORECAST.ETS.SEASONALITY(\$C\$5:\$C\$16,\$B\$5:\$B\$16,1)`

Where,

\$C\$5:\$C\$16 = The dependent array or range of data (Score column as y values)

\$B\$5:\$B\$16 = The independent array or range of data (ID column as x values)

1 = Alpha statistic type (this numeric argument can be anything from 1 to 8 based on the requirement)

## Conclusion

This article explained in detail how to use the FORECAST and other forecasting function in Excel with examples. I hope this article has been very beneficial to you. Feel free to ask if you have any questions regarding the topic.

## Related Articles #### Sanjida Ahmed

Hello World! This is Sanjida, an Engineer who is passionate about researching real-world problems and inventing solutions that haven’t been discovered yet. Here, I try to deliver the results with explanations of Excel-related problems, where most of my interpretations will be provided to you in the form of Visual Basic for Applications (VBA) programming language. Being a programmer and a constant solution seeker, made me interested in assisting the world with top-notch innovations and evaluations of data analysis.

We will be happy to hear your thoughts 