Patent ID: 6834266
Filing Date: 2004-12-21
Classification: G06Q

Abstract:
A computer-based method of estimating a seasonality of clusters of similar items of commerce based on historical sales data values and associated error information comprising:executing on a computer the steps (A)-(E) as follows: A. receiving a first set of data containing a plurality of data points, each of which data points represent historical sales data for one or more of the items of commerce and which is expressed as a value and an associated error, B. determining a distance between the first set of data and each of one or more other sets of data, where each of the other data sets contains a plurality of data points, each of which data points represents historical sales data for one or more of the items of commerce and which is expressed as a value and an associated error, C. the determining step including measuring each distance based on the relation: di&it;â€ƒ&it;j=ChiSqr_PDF&it;(&Sum;l=1k&it;(Î¼i&it;â€ƒ&it;l-Î¼j&it;â€ƒ&it;lsl)2,k-1)whereâ€ƒdij is a measure of the distance between set of data i and of data j â€ƒChiSqr_PDF is a Chi-square distribution function â€ƒk is a number of data points in each of sets of data i and j â€ƒl is an index â€ƒ&mgr;il is a lth value of set of data i â€ƒ&mgr;jl is a lth value of set of data j â€ƒs={square root over (sil2+sjl2)}, where sil is error associated with the lth value of set of data i and sij is the error associated with the lth value of set of data j. D. clustering the first set of data with at least one of the other sets of data, the clustering step including comparing with a threshold one or more distances determined in the determining step, E. generating a composite data set that estimates the seasonality of one or more clusters of the items of commerce, where the composite data set is based on clustering performed in step (D) and is generated 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.