FORECAST.ETS
The FORECAST.ETS function predicts a future value or trend based on existing data points. It is useful for forecasting trends, patterns, or future values in time series data. Common applications include sales figures, stock prices, or weather patterns.
Syntax 🔗
=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). |
About FORECAST.ETS 🔗
The FORECAST.ETS function in Excel helps you predict future trends or values using your existing data. This function is useful for scenarios like sales projections or analyzing weather patterns. It uses time series data to identify patterns and supports strategic planning and decision-making. The function also allows you to include seasonal cycles, providing more accurate predictions when trends show periodic variations. Use the FORECAST.ETS function to leverage historical data for forecasting future developments.
Examples 🔗
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(B10:B13, B2:B9, A2:A9, 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(B8762:B9481, B2:B8761, A2:A8761, 24)
Notes 🔗
Ensure that your data points and timeline values are correctly aligned and formatted in the input arrays or ranges. Enhance forecast accuracy by including additional relevant data points, if available, and selecting an appropriate seasonal cycle value.
Questions 🔗
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.
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.
Related functions 🔗
FORECAST.LINEAR
TREND
GROWTH
FORECAST.ETS.CONFINT
FORECAST.ETS.SEASONALITY
FORECAST.ETS.STAT
SLOPE