Calculates or predicts a future value based on historical data by using a cubic smoothing spline algorithm. The cubic smoothing spline model is equivalent to an ARIMA(0,2,2) model but with a restricted parameter space which provides a smooth historical trend and a linear forecast function. 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.SPLINE(data, frequency, start, forecast, [deployment], [plot])
The AXCEL.FORECAST.SPLINE 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-mm-dd” format.
forecast Required. This is the number of periods which we would like to run the forecast.
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 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. Please note for SPLINE, the forecast horizon in FORECAST.RUN function could not be longer than the horizon used in the training.
plot Optional. default is FALSE. When it is TRUE, Axcel produces the plot inside the 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.SPLINE in an Excel cell, the IntelliSense guides you through required and optional (shown in  brackets) inputs:
In the example above, we have:
=AXCEL.FORECAST.SPLINE(A1#, 12, “2000-1-1”,6,,TRUE)
This means that our data is located at cell A1 as an array, it is monthly, i.e. frequency is 12, it starts from January 1, 2000 (2000-01-01), we would like to forecast for 6 months, we skip the deployment and request for plots. After running this command we have:
Please note that SPLINE 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 the date index, the second column is the actual values used for training of the model, and the next column is the 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 --------- Forecast method: Cubic Smoothing Spline Model Information: $beta  0.2985984 $call splinef(y = y) Error measures: ME RMSE MAE MPE MAPE MASE ACF1 Training set 0.06457252 39.95675 28.9321 0.2194395 10.06998 0.9032727 0.1308229 Forecasts: Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 Jan 2012 424.4109 373.20065 475.6211 346.09159 502.7301 Feb 2012 433.2752 338.55110 527.9993 288.40720 578.1432 Mar 2012 442.1395 292.57794 591.7011 213.40483 670.8742 Apr 2012 451.0039 237.90147 664.1062 125.09188 776.9158 May 2012 459.8682 175.78161 743.9547 25.39527 894.3411 Jun 2012 468.7325 107.00479 830.4602 -84.48230 1021.9473 Jul 2012 477.5968 32.13299 923.0607 -203.68132 1158.8750 Aug 2012 486.4612 -48.40083 1021.3232 -331.53966 1304.4620 Sep 2012 495.3255 -134.25270 1124.9037 -467.53126 1458.1822 Oct 2012 504.1898 -225.13170 1233.5113 -611.21119 1619.5908
A template for this function is available here: