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公开(公告)号：US20160217384A1
公开(公告)日：2016-07-28
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.
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公开(公告)号：US20190250569A1
公开(公告)日：2019-08-15
申请号：US16388119
申请日：2019-04-18
Applicant: SAS Institute Inc.
Inventor： Michael James Leonard , David Bruce Elsheimer , Yuelei Sui
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 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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3.发明申请SYSTEMS AND METHODS FOR TIME SERIES ANALYSIS TECHNIQUES UTILIZING COUNT DATA SETS 审中-公开用于时间序列分析技术的系统和方法利用数据数据集公开(公告)号：US20160275399A1
公开(公告)日：2016-09-22
申请号：US15167238
申请日：2016-05-27
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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公开(公告)号：US20220350944A1
公开(公告)日：2022-11-03
申请号：US17730476
申请日：2022-04-27
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Yang Zhao , Sylvie T. Kabisa , David Bruce Elsheimer
IPC: G06F30/23
Abstract: One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.
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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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Patent Agency Ranking
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：US20140257778A1
公开(公告)日：2014-09-11
申请号：US13786838
申请日：2013-03-06
Applicant: SAS INSTITUTE INC.
Inventor： Michael Leonard , Michele Angelo Trovero , David Bruce Elsheimer , Peter Dillman
IPC: G06N99/00
Abstract: Systems and methods for forecasting ratios in hierarchies are provided. Hierarchies can be formed that have components, including a numerator time series with values from input data, a denominator time series with values from input data, and a ratio time series of the numerator time series over the denominator time series. The components can be modeled to generate forecasted hierarchies. The forecasted hierarchies can be reconciled so that the forecasted hierarchies are statistically consistent throughout nodes of the forecasted hierarchies.
Abstract translation: 提供了层次上预测比率的系统和方法。 可以形成具有分量的分层，包括具有来自输入数据的值的分子时间序列，来自输入数据的值的分母时间序列以及分母时间序列上的分子时间序列的比率时间序列。 可以对组件进行建模以生成预测的层次结构。 可以调整预测的层次结构，以便预测的层次结构在预测层次结构的整个节点上统计一致。
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公开(公告)号：US12210954B2
公开(公告)日：2025-01-28
申请号：US18530798
申请日：2023-12-06
Applicant: SAS Institute Inc.
Inventor： Sylvie Tchumtchoua Kabisa , Xilong Chen , Gunce Eryuruk Walton , David Bruce Elsheimer , Ming-Chun Chang
Abstract: A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.
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公开(公告)号：US12165031B2
公开(公告)日：2024-12-10
申请号：US18529014
申请日：2023-12-05
Applicant: SAS Institute Inc.
Inventor： Sylvie Tchumtchoua Kabisa , Xilong Chen , Gunce Eryuruk Walton , David Bruce Elsheimer , Ming-Chun Chang
Abstract: A treatment model trained to compute an estimated treatment variable value for each observation vector of a plurality of observation vectors is executed. Each observation vector includes covariate variable values, a treatment variable value, and an outcome variable value. An outcome model trained to compute an estimated outcome value for each observation vector using the treatment variable value for each observation vector is executed. A standard error value associated with the outcome model is computed using a first variance value computed using the treatment variable value of the plurality of observation vectors, using a second variance value computed using the treatment variable value and the estimated treatment variable value of the plurality of observation vectors, and using a third variance value computed using the estimated outcome value of the plurality of observation vectors. The standard error value is output.
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公开(公告)号：US11501041B1
公开(公告)日：2022-11-15
申请号：US17730476
申请日：2022-04-27
Applicant: SAS Institute Inc.
Inventor： Xilong Chen , Yang Zhao , Sylvie T. Kabisa , David Bruce Elsheimer
IPC: G06F30/23 , G06F111/10 , G06F30/27
Abstract: One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.
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