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公开(公告)号：US10884383B2
公开(公告)日：2021-01-05
申请号：US16388119
申请日：2019-04-18
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , David Bruce Elsheimer , Yuelei Sui
IPC: G05B13/02 , G05B13/04 , G06F17/16 , G05B11/01 , G05B23/02 , G06F17/18 , G06F16/00 , G05B15/02 , G06N3/04
Abstract: Machines can be controlled using advanced control systems that implement an automated version of singular spectrum analysis (SSA). For example, a control system can perform SSA on a time series having one or more time-dependent variables by: generating a trajectory matrix from the time series, performing singular value decomposition on the trajectory matrix to determine elementary matrices; and categorizing the elementary matrices into groups. The elementary matrices can be automatically categorized into the groups by: generating one or more w-correlation matrices based on spectral components associated with the time series, determining w-correlation values based on the one or more w-correlation matrices; categorizing the w-correlation values into a predefined number of w-correlation sets, and forming the groups based on the predefined number of w-correlation sets. The control system can then generate a predictive forecast using the groups and control operation of a machine using the predictive forecast.
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公开(公告)号：US20190146849A1
公开(公告)日：2019-05-16
申请号：US16193661
申请日：2018-11-16
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Thiago Santos Quirino , Edward Tilden Blair , Jennifer Leigh Sloan Beeman , David Bruce Elsheimer
CPC classification number: G06F9/5072 , G06F8/41 , G06F9/5016 , G06F9/5083 , H04L67/10
Abstract: Timestamped data can be read in parallel by multiple grid-computing devices. The timestamped data, which can be partitioned into groups based on time series criteria, can be deterministically distributed across the multiple grid-computing devices based on the time series criteria. Each grid-computing device can sort and accumulate the timestamped data into a time series for each group it receives and then process the resultant time series based on a previously distributed script, which can be compiled at each grid-computing device, to generate output data. The grid-computing devices can write their output data in parallel. As a result, vast amounts of timestamped data can be easily analyzed across an easily expandable number of grid-computing devices with reduced computational expense.
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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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公开(公告)号：US20170061315A1
公开(公告)日：2017-03-02
申请号：US15146697
申请日：2016-05-04
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Yung-Hsin Chien , Pu Wang , Yue Li
CPC classification number: G06N7/005 , G06F16/2462 , H04L67/00
Abstract: Disclosed are methods, system, and computer program products useful for generating summary statistics for data predictions based on the aggregation of data from past time intervals. Summary statistics such as prediction standard errors, variances, confidence limits, and other statistical measures, may be generated in a way that preserves the basic distributional properties of the original data sets, to allow, for example, a reduction of the multiple data sets through the aggregation process, which may be useful for a prediction process, while determining statistical information for the predicted data.
Abstract translation: 公开的方法，系统和计算机程序产品可用于基于从过去时间间隔的数据聚合生成数据预测的汇总统计数据。 可以以保持原始数据集的基本分布特性的方式产生诸如预测标准误差，方差，置信限度和其他统计度量之类的汇总统计数据，以允许例如通过 聚合过程，其可以用于预测过程，同时确定预测数据的统计信息。
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Patent Agency Ranking
公开(公告)号：US10331490B2
公开(公告)日：2019-06-25
申请号：US16193661
申请日：2018-11-16
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Thiago Santos Quirino , Edward Tilden Blair , Jennifer Leigh Sloan Beeman , David Bruce Elsheimer
Abstract: Timestamped data can be read in parallel by multiple grid-computing devices. The timestamped data, which can be partitioned into groups based on time series criteria, can be deterministically distributed across the multiple grid-computing devices based on the time series criteria. Each grid-computing device can sort and accumulate the timestamped data into a time series for each group it receives and then process the resultant time series based on a previously distributed script, which can be compiled at each grid-computing device, to generate output data. The grid-computing devices can write their output data in parallel. As a result, vast amounts of timestamped data can be easily analyzed across an easily expandable number of grid-computing devices with reduced computational expense.
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公开(公告)号：US10082774B2
公开(公告)日：2018-09-25
申请号：US15883285
申请日：2018-01-30
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , David Bruce Elsheimer
CPC classification number: G05B13/0255 , G05B11/01 , G05B13/0265 , G05B13/048 , G05B15/02 , G05B23/0283 , G06F16/00 , G06F17/16 , G06F17/18 , G06N3/0445
Abstract: Machines can be controlled using advanced control systems. Such control systems may use an automated version of singular spectrum analysis to control a machine. For example, a control system can perform singular spectrum analysis on a time series by: generating a trajectory matrix from the time series, performing singular value decomposition on the trajectory matrix to determine elementary matrices and corresponding eigenvalues, and automatically categorizing the elementary matrices into groups. The elementary matrices can be automatically categorized into the groups by: generating a matrix of w-correlation values based on the eigenvalues, categorizing the w-correlation values into a predefined number of w-correlation sets, and forming the groups based on the predefined number of w-correlation sets. The control system can then determine component time-series based on the groups, and generate a predictive forecast using the component time-series. The control system can use the predictive forecast to control operation of the machine.
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18.发明申请公开(公告)号：US20180260106A1
公开(公告)日：2018-09-13
申请号：US15976052
申请日：2018-05-10
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , Jie Zhong , Kyungduck Cha , Rajendra Singh Solanki , Rajib Nath , Macklin Frazier , Li Xu
IPC: G06F3/0484 , G06F3/0482 , G06T11/20
CPC classification number: G06F3/04847 , G06F3/0482 , G06F16/2462 , G06N20/00 , G06T11/206 , G06T2200/24
Abstract: Time-series projections can be analyzed and manipulated via an interactive graphical user interface generated by a system. The graphical user interface can include a graph depicting an aggregated time-series projection (ATSP) over a future time. The ATSP can be generated by aggregating multiple time-series. The system can receive user input indicating that an existing value in the ATSP is to be overridden with an override value. In response, the system can adjust the ATSP using the override value to generate an updated version of the ATSP. The system can display the updated version of the ATSP in the graphical user interface. The system can also propagate the impact of overriding the existing value with the override value through the multiple time-series. The system can display an impact analysis portion within the graphical user interface indicating the impact of overriding the existing value with the override value on the multiple 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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20.发明授权Systems and methods for time series analysis techniques utilizing count data sets 有权使用计数数据集的时间序列分析技术的系统和方法公开(公告)号：US09418339B1
公开(公告)日：2016-08-16
申请号：US14948970
申请日：2015-11-23
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , David Bruce Elsheimer
Abstract: Systems and methods are included for adjusting a set of predicted future data points for a time series data set including a receiver for receiving a time series data set. One or more processors and one or more non-transitory computer readable storage mediums containing instructions may be utilized. A count series forecasting engine, utilizing the one or more processors, generates a set of counts corresponding to discrete values of the time series data set. An optimal discrete probability distribution for the set of counts is selected. A set of parameters are generated for the optimal discrete probability distribution. A statistical model is selected to generate a set of predicted future data points. The set of predicted future data points are adjusted using the generated set of parameters for the optimal discrete probability distribution in order to provide greater accuracy with respect to predictions of future data points.
Abstract translation: 包括用于调整包括用于接收时间序列数据集的接收器的时间序列数据集的一组预测未来数据点的系统和方法。 可以使用包含指令的一个或多个处理器和一个或多个非暂时性的计算机可读存储介质。 利用一个或多个处理器的计数序列预测引擎生成对应于时间序列数据集的离散值的一组计数。 选择该组计数的最佳离散概率分布。 为最佳离散概率分布生成一组参数。 选择统计模型以生成一组预测的未来数据点。 使用所生成的用于最佳离散概率分布的参数集来调整预测未来数据点的集合，以便相对于未来数据点的预测提供更高的准确性。
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