The FORECAST.ETS function is used to predict a future value or trend based on existing data points. This function is particularly helpful in forecasting trends, patterns, or future values in time series data, such as sales figures, stock prices, or weather patterns.


=FORECAST.ETS(values, timeline, new_timeline, seasonality, [data], [aggregation])

values An array or range of known data points for which you want to forecast the future values.
timeline An array or range of corresponding timeline values for the known data points. These can be dates or time intervals.
new_timeline An array or range of timeline values for which you want to predict future values.
seasonality An integer specifying the number of data points in each seasonal cycle. For example 12 for monthly data with a yearly seasonality.
data (Optional) An optional array or range of additional data that can help improve the forecast accuracy.
aggregation (Optional) An optional parameter specifying the aggregation method used to combine the data. Defaults to 1 (monthly).


With the FORECAST.ETS function in Excel, you enter a world of predictive capabilities, enabling you to anticipate future trends or values with precision. This function becomes a go-to tool for forecasting a variety of scenarios, from sales projections to weather patterns, by leveraging existing data trends effectively. By harnessing time series data, you can unravel patterns and make informed decisions based on these insights, fostering strategic planning and decision-making across various domains in business and beyond. The flexibility of the FORECAST.ETS function accommodates the inclusion of seasonal cycles, allowing for more accurate predictions in scenarios where trends exhibit periodic variations. This function empowers users to unlock the potential of historical data, transforming past occurrences into a roadmap for future developments.


Suppose you have quarterly sales data for the past two years and wish to predict sales for the next year. Your known data points and timeline are in columns A and B, and you want to forecast the next four quarters. The FORECAST.ETS formula would be: =FORECAST.ETS(A2:A9, B2:B9, B10:B13, 4)

Imagine you have hourly temperature data for a year and want to forecast temperatures for the following month. The known temperature values and timeline are in columns A and B, and you aim to predict the next 720 hourly values. The FORECAST.ETS formula would be: =FORECAST.ETS(A2:A8761, B2:B8761, B8762:B9481, 24)


How does the FORECAST.ETS function predict future values?

The FORECAST.ETS function predicts future values by analyzing the existing data points and their corresponding timeline values to identify trends and patterns. It uses this historical information, along with the specified seasonality, to extrapolate and generate forecasts for the new timeline values provided.

Can additional data improve the accuracy of forecasts using the FORECAST.ETS function?

Yes, providing additional data through the optional data argument can enhance the accuracy of forecasts generated by the FORECAST.ETS function. These supplementary data points can help refine the predictions by capturing more nuances and variations in the underlying trend.

What does the seasonality parameter represent in the FORECAST.ETS function?

The seasonality parameter in the FORECAST.ETS function indicates the number of data points in each seasonal cycle. It assists the function in extrapolating future values based on recurring patterns within the data set. For instance, if the data exhibits monthly seasonality, the seasonality value would typically be 12.

In what scenarios is the FORECAST.ETS function particularly useful?

The FORECAST.ETS function proves invaluable in scenarios where historical time series data is available, and there is a need to forecast future trends, patterns, or values based on this data. It finds applications in various fields such as finance, marketing, economics, and meteorology.

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