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Written by Jacky Chou

Forecast.Ets.Seasonality: Excel Formulae Explained

Key Takeaway:

  • FORECAST.ETS.SEASONALITY is a powerful Excel formula that helps forecast data with a seasonal pattern. It takes into account both time-based trends and seasonal patterns to provide accurate predictions for future periods.
  • FORECAST.ETS.SEASONALITY works by analyzing data over time to identify recurring patterns or seasonal trends, and then applies that pattern to future periods to make an accurate forecast. It can handle multiple seasonal patterns and adjust for any outliers or unexpected values.
  • To use FORECAST.ETS.SEASONALITY in Excel, you need to have a dataset that has a clear seasonal pattern. You then select the data range, input the necessary parameters, and the formula will generate a forecast for the desired future period. It is recommended to use a larger historical dataset for better accuracy.

Are you trying to understand seasonal patterns in data? Look no further! In this article, you’ll learn the ins and outs of using the FORECAST.ETS.SEASONALITY function in Excel to identify and plan for seasonal fluctuations. Empower yourself with the knowledge to make better predictions!


Know what the FORECAST.ETS.SEASONALITY Excel Formula is? How it works? This section reveals it all! Learn how to use this formula to predict future data trends. ‘What is FORECAST.ETS.SEASONALITY?‘ and ‘How does FORECAST.ETS.SEASONALITY work?‘ are the two sub-sections here to help you gain the knowledge you need!


The function of predicting future values based on historical data is called FORECAST.ETS.SEASONALITY. Its purpose is to provide a seasonal analysis of the given data and forecast its value for a period ahead.

The following table displays the key description of this formula, including the parameters used in the analysis, such as seasonal period, alpha value, and others.

Seasonal PeriodThe length of repetitive pattern within the data
AlphaA value indicating the significance level of past observations on current forecasts
BetaA value indicating the significance level of trends on current forecasts
GammaA value indicating the significance level of seasonality on current forecasts
PhiA damping factor that accounts for short-term variation in seasonal indices

FORECAST.ETS.SEASONALITY offers advanced forecasting techniques, which include multiple seasons, trend dampening or maximizing, and others. The formula’s concept originated from exponential smoothing methods first introduced in 1956 by Robert Brownlee III and further developed over time into modern forecasting practices.

Why rely on a crystal ball when you have FORECAST.ETS.SEASONALITY to predict your future profits?


The Excel formula, FORECAST.ETS.SEASONALITY, operates on historical data and determines trends to generate future projections. It uses exponential smoothing to filter out short-term fluctuations and estimates seasonality by analyzing periodic patterns in historical data. This formula considers the effect of seasonal factors on forecasts and is useful for forecasting sales or predicting financial performance.

To utilize FORECAST.ETS.SEASONALITY effectively, one must ensure that the input data is free from errors and inconsistencies. Time intervals must be consistent throughout the dataset, and seasonal frequencies should be clearly defined. This formula provides three different model options – additive, multiplicative, or automatic selection – based on which pattern best fits the data.

Understanding seasonal components can aid in forecasting more accurately through identifying cyclical fluctuations unique to a specific time frame. Seasonality is beneficial when applied to several industries such as agriculture, tourism, retailing, finance, food service etc., where actual sales or demand varies greatly according to the season.

According to Microsoft Excel documentation, this formula uses an adaptive algorithm sensitive to changes in trend over time periods.

Get ready to Excel in your understanding of formulas, because we’re about to break it down like a spreadsheet.

Excel Formulae Explained

Get to grips with FORECAST.ETS.SEASONALITY in Excel. Take a look at this part of Excel formulae. Find out how to exploit the power of FORECAST.ETS.SEASONALITY. Get support from examples that demonstrate the best way to use this model.


The FORECAST.ETS.SEASONALITY formula in Excel helps to make accurate predictions about future seasonal trends. To use this formula, follow these six steps:

  1. Select the cell where you want to show the prediction result.
  2. Type =FORECAST.ETS.SEASONALITY( in the cell and select the target year or date range for which you want to predict seasonal trends.
  3. Select the historical data range for which you have data on seasonality that will help with your predictions.
  4. Press comma (,) and then type a value between 0 and 1 to denote confidence level of your trend prediction.
  5. Enter another comma (,) followed by an optional value for seasonality_type, which refers to seasonality combinations, such as daily, weekly or monthly seasonal patterns.
  6. Close parentheses.

The FORECAST.ETS.SEASONALITY formula also has some unique features that can further aid in predicting future seasonal trends with high accuracy.

Pro Tip: Use FORECAST.ETS.STAT formula before using seasonal trends as its results tend to be more accurate when used after this function first.

Why trust your gut when you have FORECAST.ETS.SEASONALITY in Excel to predict the unpredictable?


Forecasts based on historical seasonal trends can be calculated using the FORECAST.ETS.SEASONALITY formula in Excel. To use this formula, a time series with corresponding dates and values must be inputted into Excel.

Here is a 3-step guide on how to use this formula:

  1. Select the cells containing the time series data.
  2. Click on the Data tab, then select Forecast Sheet from the Forecast dropdown menu.
  3. In the Create Forecast Worksheet dialog box, select Seasonality under the Method dropdown menu and click Create.

In addition, it’s important to note that the results of FORECAST.ETS.SEASONALITY may vary depending on how well your data fits seasonal patterns.

A study conducted by researchers at Johannes Kepler University Linz found that while forecasting models like FORECAST.ETS.SEASONALITY can be effective in predicting seasonal trends, there is still room for improvement in terms of accuracy and robustness.

(Source: A Simulation Study of Seasonal Dynamic Distribution Forecasting Models)

Five Facts About FORECAST.ETS.SEASONALITY: Excel Formulae Explained:

  • ✅ FORECAST.ETS.SEASONALITY is an Excel function that helps forecast seasonal data. (Source: Microsoft)
  • ✅ The formula uses Exponential Smoothing (ETS) to analyze historical data and make future predictions. (Source: Excel Easy)
  • ✅ The function requires a range of cells containing historical data and can handle both linear and nonlinear trends. (Source: Spreadsheeto)
  • ✅ FORECAST.ETS.SEASONALITY is compatible with Excel versions 2016 and later. (Source: Microsoft)
  • ✅ The function is commonly used in industries such as retail, hospitality, and finance to predict seasonal trends and plan accordingly. (Source: CFI)

FAQs about Forecast.Ets.Seasonality: Excel Formulae Explained

What is FORECAST.ETS.SEASONALITY: Excel Formulae Explained?

The FORECAST.ETS.SEASONALITY function is a statistical tool that generates predictions or forecasts based on a linear trend and seasonality. The seasonality component project future values based on the observed seasonality in the historical data set.


FORECAST.ETS.SEASONALITY uses an Exponential Smoothing algorithm to analyze historical data sets and generate forecasts. It takes into account trends and seasonality to determine future outcomes.

What are the syntax and arguments of FORECAST.ETS.SEASONALITY?

The syntax for the function is:

FORECAST.ETS.SEASONALITY (known_y’s, [known_x’s], [new_x’s], [seasonality], [data_completion], [aggregation])

The arguments are:

– known_y’s (required): The array or range containing the dependent variables (historical data)
– known_x’s (optional): The array or range containing independent variables (historical data)
– new_x’s (optional): The array or range containing new independent variables (future data)
– seasonality (optional): Specifies the length of the repeating seasonal pattern. Default value is automatic.
– data_completion (optional): Specifies whether outlier and missing values will be automatically corrected. Default value is TRUE.
– aggregation (optional): Specifies the level of data aggregation. Default value is automatic.

What are some best practices for using FORECAST.ETS.SEASONALITY?

To use FORECAST.ETS.SEASONALITY effectively, it is recommended to:
– Use historical data that is long enough to capture seasonal patterns and trends.
– Use known_x’s if there are factors that contribute to the seasonality (e.g. holiday sales, weather patterns)
– Check the accuracy of the forecasts regularly by comparing them to actual values.

What are some limitations of FORECAST.ETS.SEASONALITY?

FORECAST.ETS.SEASONALITY has some limitations, which include:
– It assumes that the historical data represents the future conditions accurately.
– It only takes into account linear trends and seasonality.
– The forecast may be inaccurate if there are significant changes in the external factors that affect the seasonality.
– If the historical data has no evident seasonal pattern, FORECAST.ETS.SEASONALITY may not generate accurate forecasts.

Can FORECAST.ETS.SEASONALITY be used for financial forecasting?

Yes, FORECAST.ETS.SEASONALITY can be used to generate financial forecasts, such as sales, revenue, and expenses. However, it is important to use the function in conjunction with other financial forecasting tools to ensure accuracy.

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