Methods for estimating the seasonality of groups of similar items of commerce data sets based on historical sales data values and associated error information
First Claim
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1. 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;
where
dij is a measure of the distance between set of data i and of data j
ChiSqr_PDF is a Chi-square distribution function
k is a number of data points in each of sets of data i and j
l is an index
μ
il is a lth value of set of data i
μ
jl is a lth value of set of data j
s={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.
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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.
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Citations
10 Claims
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1. 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:
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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;
where
dij is a measure of the distance between set of data i and of data j
ChiSqr_PDF is a Chi-square distribution function
k is a number of data points in each of sets of data i and j
l is an index
μ
il is a lth value of set of data i
μ
jl is a lth value of set of data j
s={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. - View Dependent Claims (2, 3, 4)
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5. 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 steps (A)-(C) as follows A. determining distances between respective pairs of data sets in a plurality of data sets, where each data set contains a plurality of data points forming a time-series of retail data, where each data point is expressed as a value and an associated error, B. the determining step including measuring each distance as a function of the relation -
j = ChiSqr_PDF ( ∑ l = 1 k ( μ i l - μ j l s l ) 2 , k - 1 ) where
dij is a measure of the distance between set of data i and set of data j
Chisqr_PDF is a Chi-square distribution function
k is a number of data points in/each of sets of data i and j
l is an index
μ
u is a lth value of set of data i
μ
jl is a lth value of set of data j
s=√
{square root over (sil2+sjl2)}, where sil is error associated with the lth value of set of data i and sjl is the error associated with the lth value of set of data j, andC. estimating the seasonality of one or more clusters of the items of commerce based on clustering the plurality of data sets as a function of the distances determined in the determining step, the clustering step including forming one or more composite data sets, each containing a plurality of data points forming a time-series of retail data based on data points contained in data sets whose distances compared favorably with a threshold. - View Dependent Claims (6, 7, 8, 9, 10)
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Specification