Patents by Inventor Jerzy Michal Brzezicki
Jerzy Michal Brzezicki has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 10685283Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.Type: GrantFiled: December 24, 2019Date of Patent: June 16, 2020Assignee: SAS INSTITUTE INC.Inventors: Yue Li, Michele Angelo Trovero, Phillip Mark Helmkamp, Jerzy Michal Brzezicki, Macklin Carter Frazier, Timothy Patrick Haley, Randy Thomas Solomonson, Sangmin Kim, Steven Christopher Mills, Yung-Hsin Chien, Ron Travis Hodgin, Jingrui Xie
-
Publication number: 20200143246Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.Type: ApplicationFiled: December 24, 2019Publication date: May 7, 2020Applicant: SAS Institute Inc.Inventors: YUE LI, MICHELE ANGELO TROVERO, PHILLIP MARK HELMKAMP, JERZY MICHAL BRZEZICKI, MACKLIN CARTER FRAZIER, TIMOTHY PATRICK HALEY, RANDY THOMAS SOLOMONSON, SANGMIN KIM, STEVEN CHRISTOPHER MILLS, YUNG-HSIN CHIEN, RON TRAVIS HODGIN, JINGRUI XIE
-
Patent number: 10560313Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.Type: GrantFiled: June 26, 2019Date of Patent: February 11, 2020Assignee: SAS INSTITUTE INC.Inventors: Udo Vincenzo Sglavo, Phillip Mark Helmkamp, Jerzy Michal Brzezicki, Timothy Patrick Haley, Sujatha Pothireddy
-
Publication number: 20190394083Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.Type: ApplicationFiled: June 26, 2019Publication date: December 26, 2019Applicant: SAS Institute Inc.Inventors: Udo Vincenzo Sglavo, Phillip Mark Helmkamp, Jerzy Michal Brzezicki, Timothy Patrick Haley, Sujatha Pothireddy
-
Patent number: 10037305Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: GrantFiled: February 6, 2018Date of Patent: July 31, 2018Assignee: SAS INSTITUTE INC.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Sujatha Pothireddy
-
Patent number: 10025753Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: GrantFiled: February 6, 2018Date of Patent: July 17, 2018Assignee: SAS INSTITUTE INC.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Sujatha Pothireddy
-
Publication number: 20180157619Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: ApplicationFiled: February 6, 2018Publication date: June 7, 2018Applicant: SAS Institute Inc.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Sujatha Pothireddy
-
Publication number: 20180157620Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: ApplicationFiled: February 6, 2018Publication date: June 7, 2018Applicant: SAS Institute Inc.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Sujatha Pothireddy
-
Patent number: 9916282Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: GrantFiled: June 10, 2015Date of Patent: March 13, 2018Assignee: SAS INSTITUTE INC.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Ranbir Singh Tomar, Kannukuzhiyil Kurien Kurien, Sujatha Pothireddy, Rajib Nath, Vilochan Suresh Muley
-
Publication number: 20150278153Abstract: Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.Type: ApplicationFiled: June 10, 2015Publication date: October 1, 2015Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Ranbir Singh Tomar, Kannukuzhiyil Kurien Kurien, Sujatha Pothireddy, Rajib Nath, Vilochan Suresh Muley
-
Patent number: 9087306Abstract: Systems and methods are provided for analyzing unstructured time stamped data of a physical process in order to generate structured hierarchical data for a hierarchical time series analysis application. A plurality of time series analysis functions are selected from a functions repository. Distributions of time stamped unstructured data are analyzed to identify a plurality of potential hierarchical structures for the unstructured data with respect to the selected time series analysis functions.Type: GrantFiled: July 13, 2012Date of Patent: July 21, 2015Assignee: SAS Institute Inc.Inventors: Michael James Leonard, Edward Tilden Blair, Jerzy Michal Brzezicki, Udo V. Sglavo, Ranbir Singh Tomar, Kannukuzhiyil Kurien Kurien, Sujatha Pothireddy, Rajib Nath, Vilochan Suresh Muley
-
Patent number: 9047559Abstract: 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.Type: GrantFiled: April 5, 2012Date of Patent: June 2, 2015Assignee: SAS Institute Inc.Inventors: Jerzy Michal Brzezicki, Dinesh P. Apte, Michael J. Leonard, Michael Ryan Chipley, Sagar Arun Mainkar, Edward Tilden Blair
-
Publication number: 20150120263Abstract: 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.Type: ApplicationFiled: December 1, 2014Publication date: April 30, 2015Inventors: Jerzy Michal Brzezicki, Dinesh P. Apte, Michael J. Leonard, Michael Ryan Chipley, Sagar Arun Mainkar, Edward Tilden Blair
-
Publication number: 20140019088Abstract: Systems and methods are provided for analyzing unstructured time stamped data of a physical process in order to generate structured hierarchical data for a hierarchical time series analysis application. A plurality of time series analysis functions are selected from a functions repository. Distributions of time stamped unstructured data are analyzed to identify a plurality of potential hierarchical structures for the unstructured data with respect to the selected time series analysis functions.Type: ApplicationFiled: July 13, 2012Publication date: January 16, 2014Inventors: Michael James LEONARD, Edward Tilden BLAIR, Jerzy Michal BRZEZICKI, Udo V. SGLAVO, Ranbir Singh TOMAR, Kannukuzhiyil Kurien KURIEN, Sujatha POTHIREDDY, Rajib NATH, Vilochan Suresh MULEY
-
Publication number: 20130238399Abstract: Computer-implemented systems and methods are provided for implementing a scenario analysis manager that performs multiple scenarios based upon time series data that is representative of transactional data are provided. A system and method provides candidate predictive models for a first scenario for selection where the set of candidate predictive models includes an identification of variables associated with a model. Model selection data is received from a scenario analysis manager where a selected model is configured to predict a future value of a first variable based on values of a second variable. Time series data is received representative of past transaction activity of the first variable and the second variable, and data representative of a future value of the second variable is also received. The future value of the first variable is determined using the selected model, the time-series data and the future value of the second variable.Type: ApplicationFiled: February 20, 2013Publication date: September 12, 2013Applicant: SAS Institute Inc.Inventors: Michael Ryan Chipley, Michael J. Leonard, Philip Lodge Holman, Jerzy Michal Brzezicki, Karl Moss, Dinesh P. Apte
-
Publication number: 20130024167Abstract: Systems and methods are provided for evaluating a physical process with respect to one or more attributes of the physical process by combining forecasts for the one or more physical process attributes, where data for evaluating the physical process is generated over time. A forecast model selection graph is accessed, the forecast model selection graph comprising a hierarchy of nodes arranged in parent-child relationships. A plurality of model forecast nodes are resolved, where resolving a model forecast node includes generating a node forecast for the one or more physical process attributes. A combination node is processed, where a combination node transforms a plurality of node forecasts at child nodes of the combination node into a combined forecast. A selection node is processed, where a selection node chooses a node forecast from among child nodes of the selection node based on a selection criteria.Type: ApplicationFiled: July 22, 2011Publication date: January 24, 2013Inventors: Edward Tilden Blair, Michael J. Leonard, David Bruce Elsheimer, Jerzy Michal Brzezicki, Kannukuzhiyil Kurien Kurien, Michael Ryan Chipley, Dinesh P. Apte, Ming-Chun Chang
-
Publication number: 20130024173Abstract: 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.Type: ApplicationFiled: April 5, 2012Publication date: January 24, 2013Inventors: Jerzy Michal Brzezicki, Dinesh P. Apte, Michael J. Leonard, Michael Ryan Chipley, Sagar Arun Mainkar, Edward Tilden Blair
-
Publication number: 20110106723Abstract: Computer-implemented systems and methods are provided for implementing a scenario analysis manager that performs multiple scenarios based upon time series data that is representative of transactional data are provided. A system and method provides candidate predictive models for a first scenario for selection where the set of candidate predictive models includes an identification of variables associated with a model. Model selection data is received from a scenario analysis manager where a selected model is configured to predict a future value of a first variable based on values of a second variable. Time series data is received representative of past transaction activity of the first variable and the second variable, and data representative of a future value of the second variable is also received. The future value of the first variable is determined using the selected model, the time-series data and the future value of the second variable.Type: ApplicationFiled: November 3, 2009Publication date: May 5, 2011Inventors: Michael Ryan Chipley, Michael J. Leonard, Philip Lodge Holman, Jerzy Michal Brzezicki, Karl Moss, Dinesh P. Apte