Classifying, Clustering, and Grouping Demand Series

• US 20150302432A1
• Filed: 12/17/2014
• Published: 10/22/2015
• Est. Priority Date: 04/17/2014
• Status: Abandoned Application

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Chien, Yung-Hsin, Li, Yue, Wang, Pu
• Application Number:
  US14/574,142
• Publication Number:
  US 20150302432A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Systems and methods for linear regression using safe screening techniques. A computing system may receive a plurality of time series included in a forecast hierarchy. For each time series, the computing system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the forecast hierarchy at which the each individual time series comprises an aggregate demand volume greater than a threshold amount. The computing system may generate an additional forecast hierarchy using the first forecast hierarchy, the classification, the pattern group, and the level. The computing system may provide, to the user of the system, forecast information related to at least one time series based on the additional forecast hierarchy.

Claims

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

receive a plurality of time series included in a forecast hierarchy, each individual time series of the plurality of time series comprising one or more demand characteristics and a demand pattern for an item, the one or more demand characteristics including at least one of a demand lifecycle, an intermittence, or a seasonality, the demand pattern indicating one or more time intervals for which demand for the item is greater than a threshold value;

for each time series of the plurality of time series;

determine a classification for the individual time series based on the one or more demand characteristics;

determine a pattern group for the individual time series by comparing the demand pattern to demand patterns other time series in the plurality of time series; and

determine a level of the forecast hierarchy at which the each individual time series comprises an aggregate demand volume greater than a threshold amount;

generate an additional forecast hierarchy using the first forecast hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional forecast hierarchy generates more accurate demand forecasts than demand forecasts generated utilizing the forecast hierarchy; and

provide, to a user of the computer-program product, forecast information related to at least one time series of the plurality of time series based on the additional forecast hierarchy.

2. The computer-program product of claim 1, wherein the aggregate demand volume includes a summation of demand volumes of one or more time series of the plurality of time series.

3. The computer-program product of claim 1, wherein the instructions that are configured to cause the data processing apparatus to determine the classification for the individual time series based on the one or more demand characteristics are further configured to be executed to cause the data processing apparatus to:

determine a number of low-demand periods within the individual time series, wherein each of the number of low-demand periods is a time period during which demand for the item is less than a threshold value;

identify a number of cycles based on the number of low-demand periods; and

determine a preliminary classification for the time series based on the identified number of cycles and the one or more demand characteristics.

4. The computer-program product of claim 3, wherein the instructions that are configured to cause the data processing apparatus to determine the number of low-demand periods within the individual time series are further configured to be executed to cause the data processing apparatus to determine an approximate time series utilizing a segmentation algorithm and the individual time series, wherein the number of low-demand periods are determined based on the approximate time series.

5. The computer-program product of claim 3, wherein the preliminary classification comprises one of a short-history classification, a low-volume classification, a short time-span non-intermittent classification, a short time-span intermittent classification, a long time-span seasonal classification, a long time-span non-seasonal classification, a long time-span intermittent classification, a long time-span seasonal intermittent classification, or a long time-span unclassifiable classification.

6. The computer-program product of claim 1, including further instructions configured to be executed to cause a data processing apparatus to perform a horizontal reclassification of the individual time series using a classification of one or more sibling time series, the one or more sibling time series belonging to a common parent node in the first forecast hierarchy as the individual time series when the individual time series is classified as unclassifiable.

7. The computer-program product of claim 6, wherein the instructions that are configured to perform the horizontal reclassification of the time series are further configured to be executed to cause the data processing apparatus to:

determine the horizontal reclassification based on a most frequently used classification among the one or more sibling time series; and

assign the horizontal reclassification to a subset of the individual time series.

8. The computer-program product of claim 5, including further instructions configured to be executed to cause a data processing apparatus to perform a top-down reclassification of the individual time series using a parent time series of the individual time series as indicated in the first forecast hierarchy when the determined classification for the individual time series is long time-span seasonal intermittent.

9. The computer-program product of claim 1, wherein the instructions that are configured to determine the pattern group are further configured to be executed to cause the data processing apparatus to:

generate an initial set of time-series clusters using a first number of clusters, a k-means clustering algorithm, and the plurality of time series;

determine an optimal number of clusters using a hierarchical clustering technique applied to the initial set of time-series clusters; and

determine an optimal set of time-series clusters using the optimal number of clusters, the k-means clustering algorithm, and the plurality of time series.

10. (canceled)

11. A computer-implemented method comprising:

receiving a plurality of time series included in a forecast hierarchy, each individual time series of the plurality of time series comprising one or more demand characteristics and a demand pattern for an item, the one or more demand characteristics including at least one of a demand lifecycle, an intermittence, or a seasonality, the demand pattern indicating one or more time intervals for which demand for the item is greater than a threshold value;

for each time series of the plurality of time series;

determining, by a computing device, a classification for the individual time series based on the one or more demand characteristics;

determining, by the computing device, a pattern group for the individual time series by comparing the demand pattern to demand patterns other time series in the plurality of time series; and

determining, by the computing device, a level of the forecast hierarchy at which the each individual time series comprises an aggregate demand volume greater than a threshold amount;

generating, by the computing device an additional forecast hierarchy using the first forecast hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional forecast hierarchy generates more accurate demand forecasts than demand forecasts generated utilizing the first forecast hierarchy; and

providing to a user of the computer-program product, forecast information related to at least one time series of the plurality of time series based on the additional forecast hierarchy.

12-20. -20. (canceled)

21. A system, comprising:

a processor; and

a non-transitory computer-readable storage medium including instructions

configured to be executed that, when executed by the processor, cause the system to perform operations including;

receiving a plurality of time series included in a forecast hierarchy, each individual time series of the plurality of time series comprising one or more demand characteristics and a demand pattern for an item, the one or more demand characteristics including at least one of a demand lifecycle, an intermittence, or a seasonality, the demand pattern indicating one or more time intervals for which demand for the item is greater than a threshold value;

for each time series of the plurality of time series;

determining a classification for the individual time series based on the one or more demand characteristics;

determining a pattern group for the individual time series by comparing the demand pattern to demand patterns other time series in the plurality of time series; and

determining a level of the forecast hierarchy at which the each individual time series comprises an aggregate demand volume greater than a threshold amount;

generating an additional forecast hierarchy using the first forecast hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional forecast hierarchy generates more accurate demand forecasts than demand forecasts generated utilizing the first forecast hierarchy; and

providing to a user of the computer-program product, forecast information related to at least one time series of the plurality of time series based on the additional forecast hierarchy.

22. The system of claim 21, wherein the aggregate demand volume includes a summation of demand volumes of one or more time series of the plurality of time series.

23. The system of claim 21, wherein the instructions that are, when executed by the processor, configured to cause the system to perform operations including determining the classification for the individual time series based on the one or more demand characteristics, include further instructions that are configured to, when executed by the processor, cause the system to perform operations including:

determining a number of low-demand periods within the individual time series, wherein each of the number of low-demand periods is a time period during which demand for the item is less than a threshold value;

identifying a number of cycles based on the number of low-demand periods; and

a preliminary classification for the time series based on the identified number of cycles and the one or more demand characteristics.

24. The system of claim 23, wherein the instructions that are, when executed by the processor, configured to cause the system to perform operations including determining the number of low-demand periods within the individual time series, include further instructions that are configured to, when executed by the processor, cause the system to perform operations including determining an approximate time series utilizing a segmentation algorithm and the individual time series, wherein the number of low-demand periods is determined based on the approximate time series.

25. The system of claim 23, wherein the preliminary classification comprises one of a short-history classification, a low-volume classification, a short time-span non-intermittent classification, a short time-span intermittent classification, a long time-span seasonal classification, a long time-span non-seasonal classification, a long time-span intermittent classification, a long time-span seasonal intermittent classification, or a long time-span unclassifiable classification.

26. The system of claim 21, including further instructions configured to be executed that, when executed by the processor, cause the system to perform further operations including performing a horizontal reclassification of the individual time series using a classification of one or more sibling time series, the one or more sibling time series belonging to a common parent node in the first forecast hierarchy as the individual time series when the individual time series is classified as unclassifiable.

27. The system of claim 26, wherein the instructions that are, when executed by the processor, configured to cause the system to perform operations including performing the horizontal reclassification of the time series, include further instructions that are configured to, when executed by the processor, cause the system to perform operations including:

determining the horizontal reclassification based on a most frequently used classification among the one or more sibling time series; and

assigning the horizontal reclassification to a subset of the individual time series.

28. The system of claim 25, including further instructions configured to be executed that, when executed by the processor, cause the system to perform further operations including performing a top-down reclassification of the individual time series using a parent time series of the individual time series as indicated in the forecast hierarchy when the determined classification for the individual time series is long time-span seasonal intermittent.

29. The system of claim 21, wherein the instructions that are, when executed by the processor, configured to cause the system to perform operations including determining the pattern group, include further instructions that are configured to, when executed by the processor, cause the system to perform operations including:

generating an initial set of time-series clusters using a first number of clusters, a k-means clustering algorithm, and the plurality of time series;

determining an optimal number of clusters using a hierarchical clustering technique applied to the initial set of time-series clusters; and

determining an optimal set of time-series clusters using the optimal number of clusters, the k-means clustering algorithm, and the plurality of time series.

30. The system of claim 21, wherein the instructions that are, when executed by the processor, configured to cause the system to perform operations including determining the level of the forecast hierarchy, include further instructions that are configured to, when executed by the processor, cause the system to perform operations including:

determining a lowest grouping level of the forecast hierarchy;

determining a user-defined grouping level of the forecast hierarchy; and

for each level of the forecast hierarchy between, and including, the user-defined grouping level and the lowest grouping level;

determining a demand volume amount for one or more sibling time series in the forecast hierarchy; and

aggregating the one or more sibling time series in the forecast hierarchy based on the demand volume amount.