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

• US 20050102272A1
• Filed: 12/20/2004
• Published: 05/12/2005
• Est. Priority Date: 10/11/2001
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: ProfitLogic, Inc. (Oracle Corporation)
• Inventors: Kumar, Mahesh, Woo, Jonathan W., Gaidarev, Peter
• Granted Patent: US 7,437,308 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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,

clustering the first set of data with at least one of the other sets of data, the clustering step including

comparing a threshold one or more distances determined in the determining step,

generating a composite data set as a function of data points contained in the first set of data and the one or more other sets of data, if any, whose distances compared favorably with the threshold.

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 Guassian distribution.

10. The method of claim 1, in which the determining step includes 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.

11. The method of claim 10 in which the distance is measured based on