FORECAST.ETS.STAT

The FORECAST.ETS.STAT function predicts a future value based on a series of existing values. It is useful for forecasting trends in data sets. This function helps in making informed projections for various scenarios.

Syntax 🔗

=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.

About FORECAST.ETS.STAT 🔗

Use the FORECAST.ETS.STAT function in Excel to predict future values based on existing data. By inputting known data points, both dependent (y-values) and independent (x-values), along with the target date, this function helps you anticipate future trends. It applies advanced statistical methods to generate forecasts and lets you adjust for seasonality and missing data. This function is useful for decision-making and strategizing based on anticipated outcomes.

Examples 🔗

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

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

Notes 🔗

Ensure your input data is structured accurately to reflect the relationship between the independent and dependent variables. Consider incorporating relevant seasonality if applicable. Adjust the function parameters to accommodate the nature of your data set and the intended forecasting horizon.

Questions 🔗

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.

FORECAST
TREND
GROWTH
FORECAST.ETS.CONFINT
FORECAST.ETS.SEASONALITY

Leave a Comment