The FORECAST.ETS.STAT function is used in Excel to predict a future value based on a series of existing values. This function is particularly useful in forecasting trends in data sets and making informed projections for various scenarios.


=FORECAST.ETS.STAT(target_date, known_y's, known_x's, new_x's, [seasonality], [data_completion])

target_date The date for which you want to predict the value.
known_y's The array or range of known dependent y-values in the data set.
known_x's The array or range of known independent x-values in the data set.
new_x's The array or range of new independent x-values for which you want to predict corresponding y-values.
seasonality (Optional) The length of the seasonal pattern used for adjusting the forecast. Defaults to 1 if omitted.
data_completion (Optional) A logical value indicating whether missing data points are assumed to be at the end of a time period. Defaults to FALSE if omitted.


When you find yourself gazing into the crystal ball of data analysis and require a tool that can envision future values with precision, look no further than FORECAST.ETS.STAT in Excel. This function acts as a reliable seer, providing insights into potential trends and outcomes based on existing data observations. Employed in various domains for predictive purposes, FORECAST.ETS.STAT equips users with the capability to anticipate future values and foresee potential scenarios with a degree of confidence. To maximize the utility of FORECAST.ETS.STAT, you input a series of known data points – both dependent (y-values) and independent (x-values) – along with the target date for which you seek a forecast. The function then employs advanced statistical methods to generate a forecasted value based on the historical trend and relationships within the data set. Furthermore, you can customize the forecasting process by specifying the seasonality parameter to account for periodic patterns and opting for data completion adjustments to handle missing data points effectively. FORECAST.ETS.STAT excels in providing a forward-looking perspective, aiding decision-making processes, and strategizing based on anticipated outcomes. Embrace this function as your guide in navigating the realm of data forecasting, empowering you to make informed choices and steer your endeavors toward success.


Suppose you have historical sales data recorded monthly for the past year, with corresponding revenue figures and time. To predict the sales for the upcoming months, you can use the FORECAST.ETS.STAT function as follows: =FORECAST.ETS.STAT("07/01/2022", B2:B13, A2:A13, A14:A18)

Imagine you have gathered data on website traffic over the past year, segmented by month, and you want to forecast future traffic patterns. By leveraging the FORECAST.ETS.STAT function, you can input the established patterns and predict traffic for future months: =FORECAST.ETS.STAT("01/01/2023", C2:C13, A2:A13, A14:A18, 12)


How does the FORECAST.ETS.STAT function generate predictions?

The FORECAST.ETS.STAT function utilizes advanced statistical techniques to analyze the historical relationships between the known x-values (independent variables) and known y-values (dependent variables) to predict future values based on the provided target date and new x-values.

Can the FORECAST.ETS.STAT function handle missing data points?

Yes, the FORECAST.ETS.STAT function can handle missing data points using the optional data_completion argument. By setting data_completion to TRUE, the function assumes missing data at the end of a time period, enabling a more comprehensive forecast.

In what scenarios is the seasonality parameter useful in the FORECAST.ETS.STAT function?

The seasonality parameter in the FORECAST.ETS.STAT function is particularly beneficial when dealing with data sets that exhibit periodic patterns or trends over fixed intervals. By specifying the length of the seasonal pattern, the function adjusts the forecast to account for recurring fluctuations in the data.

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