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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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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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3.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：US10685283B2
公开(公告)日：2020-06-16
申请号：US16726616
申请日：2019-12-24
Applicant: SAS Institute Inc.
Inventor： 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
IPC: G06N3/08 , G06F16/2458 , G06F16/28 , G06N3/04 , G06F16/242 , G06F16/248 , G06F16/26 , 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 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.
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5.发明申请公开(公告)号：US20190317952A1
公开(公告)日：2019-10-17
申请号：US16379470
申请日：2019-04-09
Applicant: SAS Institute Inc.
Inventor： Yue Li , Neha Bindumadhav Kulkarni , Yung-Hsin Chien , Sagar Arun Mainkar , Bhupendra Suresh Bendale
Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped data.
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Patent Agency Ranking
公开(公告)号：US20160239749A1
公开(公告)日：2016-08-18
申请号：US14987982
申请日：2016-01-05
Applicant: SAS INSTITUTE INC.
Inventor： Sergiy Peredriy , Yung-Hsin Chien , Arin Chaudhuri , Ann Mary McGuirk , Yongqiao Xiao
CPC classification number: G06N5/048 , G06F16/283 , G06Q10/04 , G06Q30/0202
Abstract: Computer-implemented systems and methods are provided for predicting outputs. Global output fractions associated with an object are approximated. Outputs for a group are predicted based upon a cyclical aspect component and a movement prediction. An output prediction is calculated based upon the predicted outputs for a related object group and the approximated global output fraction for a particular object.
Abstract translation: 提供计算机实现的系统和方法来预测输出。 与对象相关联的全局输出分数近似。 基于循环方面组件和运动预测来预测组的输出。 基于相关对象组的预测输出和特定对象的近似全局输出分数来计算输出预测。
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7.发明授权公开(公告)号：US10540377B2
公开(公告)日：2020-01-21
申请号：US16379470
申请日：2019-04-09
Applicant: SAS Institute Inc.
Inventor： Yue Li , Neha Bindumadhav Kulkarni , Yung-Hsin Chien , Sagar Arun Mainkar , Bhupendra Suresh Bendale
Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped data.
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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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9.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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