Methods for estimating the seasonality of groups of similar items of commerce data sets based on historical sales date values and associated error information

• US 7,437,308 B2
• Filed: 12/20/2004
• Issued: 10/14/2008
• Est. Priority Date: 10/11/2001
• Status: Expired due to Term

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Woo, Jonathan W., Gaidarev, Peter, Kumar, Mahesh
• Primary Examiner(s): Jarrett; Scott L
• Application Number:
  US11/017,285
• Publication Number:
  US 20050102272A1
• Time in Patent Office: 1,394 Days
• Field of Search: 705/10
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Claims

1. A computer-based method of clustering data sets comprising executing on a computer the steps of:

receiving a first set of data containing a plurality of data points, each of which is expressed as a value and an associated error;

determining a distance between the first set of data and each of one or more other sets of data, where each of those other data sets contains a plurality of data points, each of which is expressed as a value and an associated error, the determining step including measuring each distance as a function, at least in part, of the error associated with one or more data points in each of the sets of data for which the distance is determined, including measuring each distance as a function of the value and the error associated with one or more data points in each of the sets of data for which the distance is determined, in which the distance is measured based on

2. The method of claim 1 in which the respective data points of each data set are related in a time-sequence.

3. The method of claim 1 in which the data points relate to a seasonality of at least one item in commerce.

4. The method of claim 1 in which each of the sets of data relates to seasonalities of items of commerce.

5. The method of claim 4 in which the items of commerce comprise retail products, the data points relate to times during a season, and the values associated with each of the data points correspond to respective ones of the retail products.

6. The method of claim 5 wherein at least one of the errors is a statistical measures of the variability of the associated value in the respective set of data.

7. The method of claim 1 in which at least one value is a statistical mean and in which the associated error is a statistical measure of a distribution with respect to that mean.

8. The method of claim 7 in which the statistical measure comprises a standard deviation.

9. The method of claim 1 in which the distribution of the values comprises a Gaussian distribution.

10. The method of claim 1 in which the clustering of data includes merging the data sets belonging to a cluster using a weighted average.

11. The method of claim 1 also including merging the seasonalities of the data sets belonging to a cluster.

12. A machine-accessible medium that when accessed results in a machine effecting actions comprising:

receiving a first set of data containing a plurality of data points, each of which is expressed as a value and an associated error;

determining a distance between the first set of data and each of one or more other sets of data, where each of those other data sets contains a plurality of data points, each of which is expressed as a value and an associated error, the determining step including measuring each distance as a function, at least in part, of the error associated with one or more data points in each of the sets of data for which the distance is determined, including measuring each distance as a function of the value and the error associated with one or more data points in each of the sets of data for which the distance is determined, in which the distance is measured based on

13. A computer program product, stored on computer readable medium, that when executed on a processor, determines composite seasonality time-series, the processor coupled to a display, the computer program product comprising:

computer program code for receiving data that represents seasonality time-series for each of a set of retail items, the data also representing error information associated with data values for each of a series of time points for each of the items, wherein the error information is used to measure a distance as a function of the error information, wherein a distance is measured as a function of the data value and the associated error information, in which the distance is measured based on