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Geoff Peters

Geoff Peters

Geoff is a Senior Software Developer on the Predictive team for SAP Analytics Cloud. Geoff has a passion for software design, implementation, and problem-solving, with a special focus in the area of user interfaces and interaction.

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Additional Forecast Inputs are calculated measures, measure input controls, or additional measures from your data model that you want to consider when creating a forecast. The option of using additional inputs is only available for Time Series charts and the chosen input requires actual values for the period selected in the Time Series chart and future values for the forecast period. The following is an example use case for the Additional Forecast Inputs feature.

Let’s say we want to forecast the energy consumption of a household for August to October 2018.

We have the energy consumption of the household and Average Outdoor Temperature from January 2017 to July 2018. Next, we have the forecasted Average Temperature for August to December 2018 to use as an additional input.

Here is the sample data we’re working with:

Month / Year Consumption (kWh) Average Temperature (Degrees C)
January 2017 578.64 2.2°C
February 2017 407.54 3.1°C
March 2017 298.42 6.5°C
April 2017 182.29 9.4°C
May 2017 182.47 13.0°C
June 2017 189.24 15.7°C
July 2017 266.05 18.4°C
August 2017 306.43 18.9°C
September 2017 228.53 16.1°C
October 2017 218.84 9.9°C
November 2017 360.61 6.9°C
December 2017 455.79 2.8°C
January 2018 430.25 5.4°C
February 2018 407.81 3.6°C
March 2018 412.37 6.2°C
April 2018 281.35 9.4°C
May 2018 240.35 14.9°C
June 2018 299.91 15.8°C
July 2018 531.86 19.3°C
August 2018 18.4°C (Forecast)
September 2018 14.3°C (Forecast)
October 2018 10.1°C (Forecast)
November 2018 7.4°C (Forecast)
December 2018     2.4°C (Forecast)

Example 1: Forecast with Additional Variable Input

Forecasting future consumption while making use of the Average Temperature as an Additional Forecast Input produces the following result:

Additional Inputs

Please note: without the forecasted temperature data for the future period (August to December 2018), we would receive an error message: “None of the additional inputs are being considered in the forecast due to a lack of future values”.

In the above Time Series Chart, the dotted line indicates the Forecast produced by the SAP Analytics Cloud predictive algorithm. The part of the forecast starting in February 2017 is called the Past Period Forecast. The Past Period Forecast helps to indicate the accuracy of the forecast. As does the shaded confidence interval shown above and below the forecasted Consumption for August, September, and October 2018.

The resulting forecasted Consumption data points for August, September, and October 2018 take into consideration the forecasted Average Temperature. Therefore, this forecast is likely more accurate than a forecast based on historical consumption alone.

Example 2: Forecast without Additional Variable Input

SAP Analytics Cloud is also able to generate a forecast without any additional variable input. Forecasting based on historical data alone produces the following result:

In the above Time Series Chart, the dotted line indicates the Forecast produced by the SAP Analytics Cloud predictive algorithm. As the Forecast does not take into consideration the Average Temperature, the forecast is based solely on the historical Consumption value and therefore is likely to be less accurate than in Example 1.

Conclusion

By making use of Additional Forecast Inputs in your Time Series Forecast, you can provide more information to the SAP Analytics Cloud forecasting algorithms, resulting in more accurate predictions.

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