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公开(公告)号：US09916282B2
公开(公告)日：2018-03-13
申请号：US14736131
申请日：2015-06-10
Applicant: SAS Institute, Inc.
Inventor： 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
CPC classification number: G06F17/10 , G06F17/18 , G06F17/30716 , G06Q10/04 , G06Q30/02
Abstract: 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.
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公开(公告)号：US10560313B2
公开(公告)日：2020-02-11
申请号：US16452791
申请日：2019-06-26
Applicant: SAS Institute Inc.
Inventor： Udo Vincenzo Sglavo , Phillip Mark Helmkamp , Jerzy Michal Brzezicki , Timothy Patrick Haley , Sujatha Pothireddy
Abstract: 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.
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公开(公告)号：US20150278153A1
公开(公告)日：2015-10-01
申请号：US14736131
申请日：2015-06-10
Applicant: SAS Institute, Inc.
Inventor： 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
IPC: G06F17/10
CPC classification number: G06F17/10 , G06F17/18 , G06F17/30716 , G06Q10/04 , G06Q30/02
Abstract: 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.
Abstract translation: 提供了用于分析非结构化时间戳数据的系统和方法。 分析时间戳数据的分布，以识别用于构造数据的多个潜在的时间序列数据层次。 可以执行潜在的时间序列数据层级的分析。 潜在的时间序列数据层级的分析可以包括确定每个潜在的时间序列数据层次的最佳时间序列频率和数据足够度量。 可以基于数据充足度量的比较来选择潜在的时间序列数据层次之一。 可以根据所选择的时间序列数据层次结构在单次读取通过中导出多个时间序列。 可以生成对应于所导出的时间序列中的至少一个的时间序列预测。
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4.发明公开公开(公告)号：US20230297809A1
公开(公告)日：2023-09-21
申请号：US18114852
申请日：2023-02-27
Applicant: SAS Institute Inc.
Inventor： Iman Vasheghani Farahani , Ron Travis Hodgin , Sujatha Pothireddy , Kaushal Lalitkumar Chauhan , Bhupendra Suresh Bendale , Harshad Dinesh Bapat , Kritika Misra
IPC: G06N3/044
CPC classification number: G06N3/044 , G06F3/04847
Abstract: Some examples can involve a system that can receive a first user selection of time series data and a second user selection of a type of forecasting model to apply to the time series data. The system can then obtain a first set of candidate values and a second set of candidate values for a first parameter and a second parameter, respectively, of the selected type of forecasting model. The candidate values may be determined based on statistical information derived from the time series data. The system can then provide the first set of candidate values and the second set of candidate values to the user, receive user selections of a first parameter value and a second parameter value, and determine whether a conflict exists between the first parameter value and the second parameter value. If so, the system can generate an output indicating that the conflict exists.
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5.发明授权公开(公告)号：US11748597B1
公开(公告)日：2023-09-05
申请号：US18114852
申请日：2023-02-27
Applicant: SAS Institute Inc.
Inventor： Iman Vasheghani Farahani , Ron Travis Hodgin , Sujatha Pothireddy , Kaushal Lalitkumar Chauhan , Bhupendra Suresh Bendale , Harshad Dinesh Bapat , Kritika Misra
IPC: G06N3/04 , G06N3/044 , G06F3/04847
CPC classification number: G06N3/044 , G06F3/04847
Abstract: Some examples can involve a system that can receive a first user selection of time series data and a second user selection of a type of forecasting model to apply to the time series data. The system can then obtain a first set of candidate values and a second set of candidate values for a first parameter and a second parameter, respectively, of the selected type of forecasting model. The candidate values may be determined based on statistical information derived from the time series data. The system can then provide the first set of candidate values and the second set of candidate values to the user, receive user selections of a first parameter value and a second parameter value, and determine whether a conflict exists between the first parameter value and the second parameter value. If so, the system can generate an output indicating that the conflict exists.
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Patent Agency Ranking
公开(公告)号：US10037305B2
公开(公告)日：2018-07-31
申请号：US15890019
申请日：2018-02-06
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Edward Tilden Blair , Jerzy Michal Brzezicki , Udo V. Sglavo , Sujatha Pothireddy
Abstract: 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.
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公开(公告)号：US20190394083A1
公开(公告)日：2019-12-26
申请号：US16452791
申请日：2019-06-26
Applicant: SAS Institute Inc.
Inventor： Udo Vincenzo Sglavo , Phillip Mark Helmkamp , Jerzy Michal Brzezicki , Timothy Patrick Haley , Sujatha Pothireddy
IPC: H04L12/24
Abstract: 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.
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公开(公告)号：US20180157619A1
公开(公告)日：2018-06-07
申请号：US15890013
申请日：2018-02-06
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Edward Tilden Blair , Jerzy Michal Brzezicki , Udo V. Sglavo , Sujatha Pothireddy
CPC classification number: G06F17/10 , G06F17/18 , G06F17/30716 , G06Q10/04 , G06Q30/02
Abstract: 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.
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公开(公告)号：US10025753B2
公开(公告)日：2018-07-17
申请号：US15890013
申请日：2018-02-06
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Edward Tilden Blair , Jerzy Michal Brzezicki , Udo V. Sglavo , Sujatha Pothireddy
Abstract: 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.
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公开(公告)号：US20180157620A1
公开(公告)日：2018-06-07
申请号：US15890019
申请日：2018-02-06
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Edward Tilden Blair , Jerzy Michal Brzezicki , Udo V. Sglavo , Sujatha Pothireddy
CPC classification number: G06F17/10 , G06F17/18 , G06F17/30716 , G06Q10/04 , G06Q30/02
Abstract: 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.
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