Apparatus and method for generating prediction model

Patent metadata

Field	Value
Publication number	US-2016125292-A1
Application number	US-201514926286-A
Country	US
Kind code	A1
Filing date	Oct 29, 2015
Priority date	Oct 30, 2014
Publication date	May 5, 2016
Grant date	—

Abstract

Disclosed herein are an apparatus for generating a prediction model and a method thereof. The apparatus for generating a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value includes a pre-processing module configured to generate pre-processed target values by calculating weighted averages of the target values for a predetermined prediction period and subtracting the weighted averages from the target values, a prediction model generation module configured to calculate prediction values of the target values of the respective instances from the plurality of instances including the pre-processed target values, and a post-processing module configured to add the weighted averages, which are subtracted in the pre-processing module, to the prediction values of the target values of the respective instances.

First claim

What is claimed is:

1 . An apparatus for generating a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value, the apparatus comprising:
a pre-processing module configured to generate pre-processed target values by calculating weighted averages of the target values based on a predetermined prediction period and subtracting the weighted averages from the target values; a prediction model generation module configured to calculate prediction values of the target values of respective instances from the plurality of instances including the pre-processed target values; and a post-processing module configured to add the weighted averages, which are subtracted in the pre-processing module, to the prediction values of the target values of the respective instances.

2 . The apparatus of claim 1 , wherein the pre-processing module calculates the weighted average of a target value based on a certain prediction period by using the target value of the certain prediction period, one or more adjacent target values which have differences with the certain prediction period within a predetermined range, and weight values of the target value of the certain period and the one or more adjacent target values.

3 . The apparatus of claim 1 , wherein the prediction model generation module calculates the prediction values of the target values of the respective instances by performing a regression analysis on the plurality of instances including the pre-processed target values.

4 . The apparatus of claim 3 , wherein the prediction model generation module includes:
a partition unit configured to partition the plurality of instances into a predetermined number of sections based on the pre-processed target values and to assign different labels to respective partitioned sections; a classifier model generation unit configured to generate a classifier model from the plurality of instances assigned the labels and to calculate a degree of membership of each instance with respect to the label by using the classifier model; and a regression model generation unit configured to generate a regression model by performing a regression analysis on the degrees of membership and the pre-processed target values and to calculate the prediction values of the target values of the respective instances by using the regression model.

5 . The apparatus of claim 4 , wherein the partition unit partitions the plurality of instances such that the number of partitioned instances of each section is equal among the respective sections within a predetermined allowable error range.

6 . The apparatus of claim 4 , wherein the classifier model generation unit generates the classifier model by using one of a Support Vector Machine algorithm, a Naive Bayesian Classification algorithm, and a Deep Learning algorithm.

7 . A method for generating a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value, the method comprising:
a pre-processing operation of generating pre-processed target values by calculating weighted averages of the target values based on a predetermined prediction period and subtracting the weighted averages from the target values; a prediction model generating operation of calculating prediction values of the target values of respective instances from the plurality of instances including the pre-processed target values; and a post-processing operation of adding the weighted averages, which are subtracted in the pre-processing operation, to the prediction values of the target values of the respective instances.

8 . The method of claim 7 , wherein the pre-processing operation calculates a weighted average of a target value based on a certain prediction period by using the target value of the certain prediction period, one or more adjacent target values which have differences with the certain prediction period within a predetermined range, and weight values of the target value of the certain period and the one or more adjacent target values.

9 . The method of claim 7 , wherein the prediction model generating operation calculates the prediction values of the target values of the respective instances by performing a regression analysis on the plurality of instances including the pre-processed target values.

10 . The method of claim 9 , wherein the prediction model generating operation includes:
a partitioning operation of partitioning the plurality of instances into a predetermined number of sections based on the pre-processed target values and assigning different labels to respective partitioned sections; a classifier model generating operation of generating a classifier model from the plurality of instances assigned the labels and calculating a degree of membership of each instance with respect to the label by using the classifier model; and a regression model generating operation of generating a regression model by performing a regression analysis on the degrees of membership and the pre-processed target values and calculating the prediction values of the target values of the respective instances by using the regression model.

11 . The method of claim 10 , wherein the partitioning operation partitions the plurality of instances such that the number of partitioned instances of each section is equal among the respective sections within a predetermined allowable error range.

12 . The method of claim 10 , wherein the classifier model generating operation generates the classifier model by using one of a Support Vector Machine algorithm, a Naive Bayesian Classification algorithm, and a Deep Learning algorithm.

13 . A computer program, combined with hardware, configured to generate a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value, the computer program stored in a recording media to perform operations comprising:
a pre-processing operation of generating pre-processed target values by calculating weighted averages of the target values for a predetermined prediction period and subtracting the weighted averages from the target values; a prediction model generating operation of calculating prediction values of the target values of respective instances from the plurality of instances including the pre-processed target values; and a post-processing operation of adding the weighted averages, which are subtracted in the pre-processing operation, to the prediction values of the target values of the respective instances.

Assignees

• Samsung Sds Co Ltd

Inventors

• Seo Ji Hyeon
• Lee Jae Young
• Shin Dong Min
• An Kyung Jun

Classifications

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N99/005

  Physics · mapped topic

• G06N20/00 Primary

  Machine learning · CPC title