지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data.
First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.
First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.
목차
1. 서론
2. 방법론
3. 실험
4. 결과
5. 결론
참고문헌
참고문헌 (20)
참고문헌 신청
1. [학술지(정기간행물)] - Abosedra, S / 2004 / On the predictive accuracy of crude oil prices / Energy Policy 32 : 1389 ~ 1393
2. [학술지(정기간행물)] - Akarca, A. T / 1997 / Detecting break in oil price series using the box-tiao method / International Advances in Economic Research 3 (2) : 217 ~ 224
3. [학술지(정기간행물)] - Amano, R. A / 1998 / Exchange rates and oil prices / Review of International Economics 6 (4) : 683 ~ 694
4. [학술지(정기간행물)] - Basher, S. A / 2006 / price risk and emerging stock markets / Global Finance Journal 17 : 224 ~ 251
5. [단행본] - Birol, F / 2004 / Analysis of the impact of high oil price on the global economy / International Energy Agency : 1 ~ 15
2. [학술지(정기간행물)] - Akarca, A. T / 1997 / Detecting break in oil price series using the box-tiao method / International Advances in Economic Research 3 (2) : 217 ~ 224
3. [학술지(정기간행물)] - Amano, R. A / 1998 / Exchange rates and oil prices / Review of International Economics 6 (4) : 683 ~ 694
4. [학술지(정기간행물)] - Basher, S. A / 2006 / price risk and emerging stock markets / Global Finance Journal 17 : 224 ~ 251
5. [단행본] - Birol, F / 2004 / Analysis of the impact of high oil price on the global economy / International Energy Agency : 1 ~ 15
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UCI(KEPA) : I410-ECN-0101-2013-530-003636933