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Improving accuracy of predictions using seasonal relationships of time series data

机译:使用时间序列数据的季节性关系提高预测的准确性

摘要

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.
机译:提供了用于在大数据集上执行数据挖掘和统计学习技术的系统和方法。更具体地说,提供了使用安全筛选技术进行线性回归的系统和方法。技术可以包括接收包括在预测层次结构中的多个时间序列,用于执行统计学习以开发改进的预测层次结构。它可以包括与多个时间序列中的每个时间序列相关联的预处理数据,其中该预处理包括使用启用网格的计算环境并行执行的任务。对于每个时间序列,系统可以确定单个时间序列的分类,单个时间序列的模式组以及每个单个时间序列包括大于阈值量的需求输出量的预测层次结构的级别。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次。

著录项

  • 公开/公告号US10474968B2

    专利类型

  • 公开/公告日2019-11-12

    原文格式PDF

  • 申请/专利权人 SAS INSTITUTE INC.;

    申请/专利号US201816209716

  • 发明设计人 YUNG-HSIN CHIEN;PU WANG;YUE LI;

    申请日2018-12-04

  • 分类号G06N20;G06N5/04;G06F16/28;G06Q30/02;

  • 国家 US

  • 入库时间 2022-08-21 11:29:51

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