Calculates or predicts a future value based on historical data by using Exponential Smoothing (ETS) algorithm. The predicted value is a continuation of historical values. You can use this function to predict sales, revenue, inventory, or consumer trends.
This function requires the data to be organized as a series of data observed at each point of time with a constant step between the different points such as monthly, weekly, or daily.
AXCEL.FORECAST.ETS(data, frequency, start, forecast, [method], [deployment], [plot])
The AXCEL.FORECAST.ETS function syntax has the following arguments:
data Required. The historical values, for which you want to forecast. If data has a header, data starts from the second row. Otherwise, the entire vector is considered for the input.
frequency Required. Frequency of your data. 365 for daily, 52 for weekly, 12 for monthly, and 4 for quarterly data. If you do not know the frequency, you can set it as 0. In this case, Axcel automatically identifies the frequency of your data.
Start Required. start date of your data in “yyyy-m-d” format.
forecast Required. This is the number of periods which we would like to run the forecast.
method Optional. Default is “ZZZ” which means automatically selected for all three elements. A three-character string identifying the method:
"N"=none, "A"=additive "M"=multiplicative "Z"=automatically selected.
First letter is for the error type with the options of “A”, “M” or “Z”; the second letter denotes the trend type with options “N”,”A”,”M” or “Z” and the third letter denotes the season type with options “N”,”A”,”M” or “Z”. For example, “ANM” means additive for error, none for trend and multiplicative for seasonality.
deployment Optional. You can define a deployment name to deploy your model. After deployment, you can use the deployed function in AXCEL.FORECAST.RUN function. Please note that the deployment name is case sensitive and should include alphabets, numbers, and non-repeating underline. You cannot use underline at the beginning or end of the filename. For instance “abc-123” or “a-b-c-123” are allowed but “abc–123”, “abc-123-“, “-abc-123” or “abc-$123” are not allowed. Depends on your subscription, you can view and restrict access to the deployed model through Axcel web application.
plot Optional. default is FALSE. When it is TRUE, Axcel produces the plot inside sidebar. You can expand the plot and show it in your browser. Please note that no plot is produced when deployment is requested. Here is an example of the plot:
when you type =AXCEL.FORECAST.ETS in an Excel cell, the IntelliSense guides you through required and optional (shows in  brackets) inputs:
In the example above, we have:
=AXCEL.FORECAST.ETS(A1:A145, 12, “2005-03-01”,6,,,TRUE)
Which means that, our data is located at cell A1 through A145, it is monthly, i.e. frequency is 12, it starts from March 1, 2005 (2005-03-01), we would like to forecast for 6 month, we skip the method for default (automatic selection), deployment and request for plots. After running this command we have:
Please note that ETS function returns dates in Excel format. As a result you may see numbers instead of dates in data_index column. In this case, you need to change the type of column to “Date” from Excel Home menu.
As shown, fitted values and forecasts are reported in the workbook. The first column is date index, the second column is the actual values used for training of the model, the next column is forecast. Axcel also reports the forecast with 80% and 95% confidence to help the process of decision making.
In the console section in the sidebar, model performance, structure, and statistical indicators are reported for validation and transparency in the process. Here is an example:
-------- TIME SERIES Summary --------- ETS(M,Ad,M) Call: ets(y = y) Smoothing parameters: alpha = 0.7096 beta = 0.0204 gamma = 1e-04 phi = 0.98 Initial states: l = 120.9939 b = 1.7705 s = 0.8944 0.7993 0.9217 1.0592 1.2203 1.2318 1.1105 0.9786 0.9804 1.011 0.8869 0.9059 sigma: 0.0392 AIC AICc BIC 1395.166 1400.638 1448.623 Training set error measures: ME RMSE MAE MPE MAPE MASE ACF1 Training set 1.567359 10.74726 7.791605 0.4357799 2.857917 0.2432573 0.03945056
A template for this function is available here: