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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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.