CAJ | 학술논문

利用Oracle数据库中的数据挖掘选件（Oracle Data Mining，ODM），并使用存储在Oracle数据库中的时间序列数据，可构建预测时间序列未来值的支持向量机（Support Vector Machines，SVM）模型。建模时，需去除时间序列中的趋势，将目标属性标准化，确定包含延迟变量窗口的尺寸，利用机器学习方法，由时间序列历史数据得出SVM预测模型。与传统时间序列预测模型相比，SVM预测模型能够揭示时间序列的非线性、非平稳性和随机性，从而得到较高的预测精度。
이용Oracle수거고중적수거알굴선건（Oracle Data Mining，ODM），병사용존저재Oracle수거고중적시간서렬수거，가구건예측시간서렬미래치적지지향량궤（Support Vector Machines，SVM）모형。건모시，수거제시간서렬중적추세，장목표속성표준화，학정포함연지변량창구적척촌，이용궤기학습방법，유시간서렬역사수거득출SVM예측모형。여전통시간서렬예측모형상비，SVM예측모형능구게시시간서렬적비선성、비평은성화수궤성，종이득도교고적예측정도。
Using Oracle Data Mining option(ODM)and the time series data stored in oracle database, the SVM(Support Vector Machines)model which is used to predict the future value of the time series can be constructed. To build SVM model, the trend in time series must be removed, and the target attribute should be normalized. The size of the time window in which including all the lag values should be determined, then the machine learning method can be used to construct a SVM prediction model according to the time series data. Comparing with the traditional time series prediction model, SVM prediction models can reveal non-linear, non-stationary and randomness of the time series, and have higher prediction accuracy.