Devices for forecasting ratios in hierarchies

• US 9,147,218 B2
• Filed: 03/06/2013
• Issued: 09/29/2015
• Est. Priority Date: 03/06/2013
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Leonard, Michael James, Trovero, Michele Angelo, Elsheimer, David Bruce, Dillman, Peter
• Primary Examiner(s): PHAN, THAI Q
• Application Number:
  US13/786,838
• Publication Number:
  US 20140257778A1
• Time in Patent Office: 937 Days
• Field of Search: 703/2, 702/3, 705/7.11, 705/7.25
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Systems and methods for forecasting ratios in hierarchies are provided. Hierarchies can be formed that have components, including a numerator time series with values from input data, a denominator time series with values from input data, and a ratio time series of the numerator time series over the denominator time series. The components can be modeled to generate forecasted hierarchies. The forecasted hierarchies can be reconciled so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

Claims

1. A computer implemented method, comprising:

forming, on a computing device, hierarchies having components that include a numerator time series with values from input data, a denominator time series with values from the input data, and a ratio time series of the numerator time series over the denominator time series;

modeling, with the computing device, the components to generate forecasted hierarchies; and

reconciling the forecasted hierarchies so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

2. The method of claim 1, further comprising:

responsive to receiving an override that includes a change to at least one of the hierarchies,

modeling the components to generate re-forecasted hierarchies; and

re-reconciling the re-forecasted hierarchies.

3. The method of claim 1, wherein modeling the components includes modeling each component independently from each other.

4. The method of claim 1, wherein modeling the components includes modeling the numerator time series and the denominator time series jointly at a node of the hierarchies.

5. The method of claim 1, wherein modeling the components includes using a ratio model prediction directly to predict a ratio time series forecast.

6. The method of claim 5, wherein reconciling the forecasted hierarchies includes using a weight associated with each of the components.

7. The method of claim 6, wherein the weight is a time-varying weight.

8. The method of claim 7, further comprising:

determining the weight using reconciled forecasts for the denominator time series and reconciled forecasts for an aggregate denominator time series.

9. The method of claim 1, wherein modeling the components includes determining the ratio time series forecast based on a numerator time series forecast and a denominator time series forecast.

10. The method of claim 9, wherein reconciling the forecasted hierarchies includes determining reconciled ratio forecasts using reconciled forecasts for the numerator time series and reconciled forecasts for the denominator time series.

11. The method of claim 9, wherein modeling the components includes deriving confidence bands for the forecasted hierarchies using a transformation or simulation.

12. The method of claim 1, wherein modeling the components includes:

pre-adjusting the numerator time series by dividing the numerator time series by the denominator time series appended with denominator time series future predictions;

modeling the pre-adjusted numerator time series to generate adjusted predictions; and

post-adjusting the adjusted predictions by multiplying the adjusted predictions by the denominator time series appended with denominator time series future predictions.

13. The method of claim 12, wherein reconciling the forecasted hierarchies includes:

determining reconciled ratio forecasts using reconciled forecasts for the numerator time series appended to actual values and reconciled forecasts for the denominator time series appended to actual values.

14. The method of claim 1, wherein forming the hierarchies includes:

aggregating the numerator time series;

aggregating the denominator time series; and

generating an aggregated ratio of the aggregated numerator time series and the aggregated denominator time series.

15. The method of claim 14, wherein modeling the components includes using ratio model prediction directly to predict a ratio forecast for the aggregated ratio.

16. The method of claim 14, wherein modeling the components includes predicting a ratio forecast for the aggregated ratio using a forecast of the aggregated numerator time series and a forecast of the aggregated denominator time series.

17. The method of claim 14, wherein modeling the components includes:

pre-adjusting the aggregated numerator time series by dividing the aggregated numerator time series by the aggregated denominator time series appended with future predictions;

modeling the pre-adjusted aggregated numerator time series to generate adjusted aggregated predictions for the numerator time series; and

post-adjusting the adjusted aggregated predictions by multiplying the adjusted aggregated predictions with the aggregated denominator time series appended with the future predictions.

18. A system, comprising:

a server device that includes;

a processor; and

a non transitory computer readable storage medium containing instructions that when executed on the processor cause the processor to perform operations including;

forming hierarchies having components that include a numerator time series with values from input data, a denominator time series with values from the input data, and a ratio time series of the numerator time series over the denominator time series;

modeling the components to generate forecasted hierarchies; and

reconciling the forecasted hierarchies so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

19. The system of claim 18, wherein the server device includes instructions configured to cause the processor to perform operations including:

responsive to receiving an override that includes a change to at least one of the hierarchies,

modeling the components to generate re-forecasted hierarchies; and

re-reconciling the re-forecasted hierarchies.

20. The system of claim 18, wherein modeling the components includes modeling each component independently from each other.

21. The system of claim 18, wherein modeling the components includes modeling the numerator time series and the denominator time series jointly at a node of the hierarchies.

22. The system of claim 18, wherein modeling the components includes using a ratio model prediction directly to predict a ratio time series forecast.

23. The system of claim 22, wherein the server device includes instructions configured to cause the processor to perform operations including:

reconciling the forecasted hierarchies by using a weight associated with each of the components.

24. The system of claim 23, wherein the weight is a time-varying weight.

25. The system of claim 24, wherein the server device includes instructions configured to cause the processor to perform operations including:

determining the weight using reconciled forecasts for the denominator time series and reconciled forecasts for aggregate denominator time series.

26. The system of claim 18, wherein modeling the components includes determining the ratio time series forecast based on a numerator time series forecast and a denominator time series forecast.

27. The system of claim 26, wherein reconciling the forecasted hierarchies includes determining reconciled ratio forecasts using reconciled forecasts for the numerator time series and reconciled forecasts for the denominator time series.

28. The system of claim 26, wherein modeling the components includes deriving confidence bands for the forecasted hierarchies using a transformation or simulation.

29. The system of claim 18, wherein the server device includes instructions configured to cause the processor to perform operations using the modeling the components including:

pre-adjusting the numerator time series by dividing the numerator time series by the denominator time series appended with denominator time series future predictions;

modeling the pre-adjusted numerator time series to generate adjusted predictions; and

post-adjusting the adjusted predictions by multiplying the adjusted predictions by the denominator time series appended with denominator time series future predictions.

30. The system of claim 29, wherein the server device includes instructions configured to cause the processor to perform operations including:

reconciling the forecasted hierarchies by determining reconciled ratio forecasts using reconciled forecasts for the numerator time series appended to actual values and reconciled forecasts for the denominator time series appended to actual values.

31. The system of claim 18, wherein the server device includes instructions configured to cause the processor to perform operations including forming the hierarchies by operations including:

aggregating the numerator time series;

aggregating the denominator time series; and

generating an aggregated ratio of the aggregated numerator time series and the aggregated denominator time series.

32. The system of claim 31, wherein modeling the components includes using ratio model prediction directly to predict a ratio forecast for the aggregated ratio.

33. The system of claim 31, wherein modeling the components includes predicting a ratio forecast for the aggregated ratio using a forecast of the aggregated numerator time series and a forecast of the aggregated denominator time series.

34. The system of claim 31, wherein the server device includes instructions configured to cause the processor to perform operations including modeling the components with operations including:

pre-adjusting the aggregated numerator time series by dividing the aggregated numerator time series by the aggregated denominator time series appended with future predictions;

modeling the pre-adjusted aggregated numerator time series to generate adjusted aggregated predictions for the numerator time series; and

post-adjusting the adjusted aggregated predictions by multiplying the adjusted aggregated predictions with the aggregated denominator time series appended with the future predictions.

35. A computer-program product tangibly embodied in a non-transitory machine readable storage medium, including instructions configured to cause a data processing apparatus to:

form hierarchies having components that include a numerator time series with values from input data, a denominator time series with values from the input data, and a ratio time series of the numerator time series over the denominator time series;

model the components to generate forecasted hierarchies; and

reconcile the forecasted hierarchies so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.

36. The computer-program product of claim 35, further comprising instructions configured to cause the data processing apparatus to:

responsive to receiving an override that includes a change to at least one of the hierarchies,

model the components to generate re-forecasted hierarchies; and

re-reconcile the re-forecasted hierarchies.

37. The computer-program product of claim 35, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for modeling each component independently from each other.

38. The computer-program product of claim 35, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for modeling the numerator time series and the denominator time series jointly at a node of the hierarchies.

39. The computer-program product of claim 35, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for using a ratio model prediction directly to predict a ratio time series forecast.

40. The computer-program product of claim 39, wherein instructions configured to cause the data processing apparatus to reconcile the forecasted hierarchies includes instructions for using a weight associated with each of the components.

41. The computer-program product of claim 40, wherein the weight is a time-varying weight.

42. The computer-program product of claim 41, further comprising instructions configured to cause the data processing apparatus to determine the weight using reconciled forecasts for the denominator time series and reconciled forecasts for aggregate denominator time series.

43. The computer-program product of claim 35, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for determining the ratio time series forecast based on a numerator time series forecast and a denominator time series forecast.

44. The computer-program product of claim 43, wherein instructions configured to cause the data processing apparatus to reconcile the forecasted hierarchies includes instructions for determining reconciled ratio forecasts using reconciled forecasts for the numerator time series and reconciled forecasts for the denominator time series.

45. The computer-program product of claim 43, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for deriving confidence bands for the forecasted hierarchies using a transformation or simulation.

46. The computer-program product of claim 43, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for:

pre-adjusting the numerator time series by dividing the numerator time series by the denominator time series appended with denominator time series future predictions;

modeling the pre-adjusted numerator time series to generate adjusted predictions; and

post-adjusting the adjusted predictions by multiplying the adjusted predictions by the denominator time series appended with denominator time series future predictions.

47. The computer-program product of claim 46, wherein instructions configured to cause the data processing apparatus to reconcile the forecasted hierarchies includes instructions for determining reconciled ratio forecasts using reconciled forecasts for the numerator time series appended to actual values and reconciled forecasts for the denominator time series appended to actual values.

48. The computer-program product of claim 35, wherein instructions configured to cause the data processing apparatus to form the hierarchies includes instructions for:

aggregating the numerator time series;

aggregating the denominator time series; and

generating an aggregated ratio of the aggregated numerator time series and the aggregated denominator time series.

49. The computer-program product of claim 48, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for using ratio model prediction directly to predict a ratio forecast for the aggregated ratio.

50. The computer-program product of claim 49, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for predicting a ratio forecast for the aggregated ratio using a forecast of the aggregated numerator time series and a forecast of the aggregated denominator time series.

51. The computer-program product of claim 49, wherein instructions configured to cause the data processing apparatus to model the components includes instructions for:

pre-adjusting the aggregated numerator time series by dividing the aggregated numerator time series by the aggregated denominator time series appended with future predictions;

modeling the pre-adjusted aggregated numerator time series to generate adjusted aggregated predictions for the numerator time series; and

post-adjusting the adjusted aggregated predictions by multiplying the adjusted aggregated predictions with the aggregated denominator time series appended with the future predictions.