![]() The FORECAST.ETS function will return errors as shown below. ![]() Note: It is better to perform aggregation before using FORECAST.ETS to make forecasting as accurate as possible. ![]() Other options are given in the table below. The default is 1, which specifies AVERAGE. The aggregation argument is optional, and controls what function is used to aggregate data points when the timeline contains duplicate values. If zero is provided, FORECAST.ETS will treat missing data points as zero. By default, FORECAST.ETS will provide missing data points by averaging neighboring data points. The data_completion argument is optional and specifies how FORECAST.ETS should handle missing data points. The number 8784 = 366 x 24, the number of hours in a leap year. Allowed values are 0 (no seasonality, use linear algorithm), 1 (calculate seasonal pattern automatically), and n (manual season length, a number between, inclusive). For example, in the example shown, data is quarterly, so seasonality is given as 4, since there are 4 quarters in a year, and the seasonal pattern is 1 year. The seasonality argument is optional and represents the length of the seasonal pattern expressed in timeline units. The timeline can also be a simple list of numeric periods, as in the example shown. For example, the timeline could be yearly, quarterly, monthly, daily, etc. The timeline, must consist of numeric values with a constant step interval. The timeline argument is the independent array or range of values, also called x values. There are four types of financial models: DCF (Discounted Cash Flow), Comps (Comparables), LBO (Leveraged Buyout), and M&A (Merger & Acquisition) models. These are existing historical values from which a prediction will be calculated. The values argument contains the dependent array or range of data, also called y values. The target_date argument represents the point on the timeline that a prediction should be calculated. Note: Cell D12 is set equal to C12 to connect the existing values to the predicted values in the chart. The chart to the right shows this data plotted in a scatter plot. As the formula is copied down the table, FORECAST.ETS returns predicted values in D13:D16, using values in column B for target date. With these inputs, the FORECAST.ETS function returns 618.29 in cell D13. Where sales (C5:C12) and periods (B5:B12) are named ranges. ![]() In the example shown above, the formula in cell D13 is: =FORECAST.ETS(B13,sales,periods,4) This is an algorithm that applies overall smoothing, trend smoothing, and seasonal smoothing. To calculate predicted values, FORECAST.ETS uses something called triple exponential smoothing. FORECAST.ETS can be used to predict numeric values like sales, inventory, expenses, etc. The FORECAST.ETS function predicts a value based on existing values that follow a seasonal trend. ![]()
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