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1.发明申请公开(公告)号：US20190108460A1
公开(公告)日：2019-04-11
申请号：US16209716
申请日：2018-12-04
Applicant: SAS Institute Inc.
Inventor： Yung-Hsin Chien , Pu Wang , Yue Li
CPC classification number: G06N20/00 , G06F16/285 , G06F2216/03 , G06N5/04 , G06Q10/063 , G06Q30/0202
Abstract: Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount. The computing system may generate an additional prediction hierarchy using the first prediction hierarchy, the classification, the pattern group, and the level.
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公开(公告)号：US10474968B2
公开(公告)日：2019-11-12
申请号：US16209716
申请日：2018-12-04
Applicant: SAS Institute Inc.
Inventor： Yung-Hsin Chien , Pu Wang , Yue Li
Abstract: Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount. The computing system may generate an additional prediction hierarchy using the first prediction hierarchy, the classification, the pattern group, and the level.
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Patent Agency Ranking
公开(公告)号：US10169720B2
公开(公告)日：2019-01-01
申请号：US15381564
申请日：2016-12-16
Applicant: SAS Institute Inc.
Inventor： Yung-Hsin Chien , Pu Wang , Yue Li
Abstract: Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount. The computing system may generate an additional prediction hierarchy using the first prediction hierarchy, the classification, the pattern group, and the level.
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公开(公告)号：US10338994B1
公开(公告)日：2019-07-02
申请号：US16165331
申请日：2018-10-19
Applicant: SAS Institute Inc.
Inventor： Jingrui Xie , Yue Li , Yung-Hsin Chien , Pu Wang
Abstract: In some examples, a processing device can receive prediction data representing a prediction. The processing device can also receive files defining abnormal data-point patterns to be identified in the prediction data. The processing device can identify at least one abnormal data-point pattern in the prediction data by executing customizable program-code in the files. The processing device can determine an override process that corresponds to the at least one abnormal data-point pattern in response to identifying the at least one abnormal data-point pattern in the prediction data. The processing device can execute the override process to generate a corrected version of the prediction data. The processing device can then adjust one or more computer parameters based on the corrected version of the prediction data.
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5.发明申请公开(公告)号：US20170228661A1
公开(公告)日：2017-08-10
申请号：US15381564
申请日：2016-12-16
Applicant: SAS Institute Inc.
Inventor： Yung-Hsin Chien , Pu Wang , Yue Li
CPC classification number: G06N99/005 , G06F17/30598 , G06F2216/03 , G06N5/04 , G06Q30/0202
Abstract: Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount. The computing system may generate an additional prediction hierarchy using the first prediction hierarchy, the classification, the pattern group, and the level.
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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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公开(公告)号：US20150302432A1
公开(公告)日：2015-10-22
申请号：US14574142
申请日：2014-12-17
Applicant: SAS Institute INC.
Inventor： Yung-Hsin Chien , Pu Wang , Yue Li
IPC: G06Q30/02
CPC classification number: G06Q30/0202
Abstract: Systems and methods for linear regression using safe screening techniques. A computing system may receive a plurality of time series included in a forecast hierarchy. For each time series, the computing system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the forecast hierarchy at which the each individual time series comprises an aggregate demand volume greater than a threshold amount. The computing system may generate an additional forecast hierarchy using the first forecast hierarchy, the classification, the pattern group, and the level. The computing system may provide, to the user of the system, forecast information related to at least one time series based on the additional forecast hierarchy.
Abstract translation: 使用安全筛选技术进行线性回归的系统和方法。 计算系统可以接收包括在预测层级中的多个时间序列。 对于每个时间序列，计算系统可以确定各个时间序列的分类，用于各个时间序列的模式组，以及每个单独时间序列包括大于阈值的总需求量的预测层级的级别 量。 计算系统可以使用第一预测层次，分类，模式组和级别来生成附加的预测层级。 计算系统可以向系统的用户提供与基于附加预测层次的至少一个时间序列相关的预测信息。
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