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公开(公告)号:US20190188611A1
公开(公告)日:2019-06-20
申请号:US15841662
申请日:2017-12-14
Applicant: Business Objects Software Limited
Inventor: Ying Wu , Paul Pallath , Paul O'Hara
CPC classification number: G06Q10/04 , G06F17/18 , G06K9/6256 , G06N5/02 , G06N5/04 , G06N20/20 , G06Q10/00 , G06Q30/0202
Abstract: A method includes receiving training data including sequential data, determining a plurality of future time points, generating a first prediction by applying a first forecasting algorithm to the training data, generating a second prediction by applying a second forecasting algorithm to the training data, extracting predicted values from the first prediction and the second prediction that corresponds to a future time point of the plurality of future time points, applying a regression model in sequence on each of the plurality of future time points to generate a final predicted value of each of the plurality of future time points, and outputting the final predicted values of the plurality of future time points.
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公开(公告)号:US11037096B2
公开(公告)日:2021-06-15
申请号:US15856814
申请日:2017-12-28
Applicant: Business Objects Software Limited
Inventor: Paul O'Hara , Ying Wu , Paul Pallath , Malte Christian Kaufmann , Orla Cullen
Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.
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公开(公告)号:US20190205828A1
公开(公告)日:2019-07-04
申请号:US15856814
申请日:2017-12-28
Applicant: Business Objects Software Limited
Inventor: Paul O'Hara , Ying Wu , Paul Pallath , Malte Christian Kaufmann , Orla Cullen
CPC classification number: G06Q10/0833 , G06N7/005
Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.
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