Sas Institute Inc.

Michael Ryan Chipley

Steven Todd Barlow

Michael James Leonard

Sagar Arun Mainkar

Dinesh P Apte

Edward Tilden Blair

Jerzy Michal Brzezicki

Udo Sglavo

Leigh Anne Ward

Ashish Mishra

Suchitra Balaso Chikhalkar

Sameer Waman Tatke

Amrut Shantaram Vaze

Kshitija Ambulgekar

Ashwini Bhalchandra Dixit

Sarika Shrotriya

Portion of a computer screen with an animated icon

Portion of a computer screen with an icon

The computing device generates a classification model providing prediction data indicating predicted users in a target population who will respond to a target stimulus according to a predefined user response category. The computing device displays in GUI a graphical representation of a generated classification model and a plurality of options each specifying one of different objectives for determining a proportion of users in the target population to expose to the target stimulus. The computing device predicts proportion data indicating the proportion of users in the target population to expose to the target stimulus based on the determined location of the cut-off. The computing device issues one or more indications as to whether to use the classification model as a basis for exposing the proportion of users in the target population to the target stimulus according to the proportion data.

User interface for assessment of classification model quality and selection of classification model cut-off score

Systems and methods are provided for evaluating performance of forecasting models. A plurality of forecasting models may be generated using a set of in-sample data. Two or more forecasting models from the plurality of forecasting models may be selected for use in generating a combined forecast. An ex-ante combined forecast may be generated for an out-of-sample period using the selected two or more forecasting models. The ex-ante combined forecast may then be compared with a set of actual out-of-sample data to evaluate performance of the combined forecast.

Computer-implemented systems and methods for testing large scale automatic forecast combinations

Systems and methods are provided for analyzing unstructured time stamped data. A first series of user display screens are provided, where the first series of user display screens are configured to be displayed in a step-wise manner so that a user can specify a first approach through a series of predetermined steps on how the unstructured data is to be structured. A second series of user display screens are provided, where the second series of user display screens are configured to be displayed in a step-wise manner so that the user can specify a second approach through the series of predetermined steps on how the unstructured data is to be structured. Tracking data enables alternate viewing of the first and second approach to facilitate a decision whether to format the unstructured time stamped data according to the first approach or the second approach.

Computer-implemented systems and methods for time series exploration using structured judgment

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Raleigh, NC, United States of America

Michael Ryan Chipley