• 대한산업공학회지
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• 제37권1호
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• pp.64-73
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• 2011

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.

• 대한조선학회논문집
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• 제58권5호
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• 경영과학
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• 제28권1호
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• 수산경영론집
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• 제43권1호
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• pp.49-61
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• 2012

The tax exemption oil for fishery is expecting that the use of oil is gradually decreasing according to the environmental change such as reductions of vessel force caused by an upswing of oil prices and reduction of fishing vessels in the recent. Such reductions in the tax exemption oil amount have a negative effect on the tax exemption oil business and the fishery infrastructure. This paper studied to provide the basic data for a stable supply thorough the facts affected in the use of the tax exemption oil and the prediction for the use of the tax exemption oil in future. This analysis drew a estimation method by Cochrane-Orcutt repeated proceeding model with an object main factors such as a price of tax exemption oil and vessel force and international oil prices and exchange rates. And this analysis also drew the use of a tax exemption oil by 2000 after set up the scenario using an estimation method drawn. For the use of the estimated tax exemption oil analyzed to decrease within about 81 percent of the present(2020), It should be considering a stability plan for tax exemption oil for fishery in future.

• 대한산업공학회지
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• 제39권1호
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• 한국컴퓨터정보학회논문지
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• 제25권5호
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• pp.47-56
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• 2020

주가지수는 한 국가의 경제 지표뿐만 아니라 투자판단의 지표로도 활용되므로 이를 예측하는 연구가 지속해서 진행되고 있다. 주가지수 예측을 하는 작업은 기술적, 경제적 및 심리적 요인 등이 반영된 것으로 예측의 정확도를 위해서는 복합적 요인을 고려해야 한다. 따라서 지수의 변동에 영향을 미치는 요인들을 선별하여 반영한 주가지수 예측모델연구가 필요하다. 이와 관련한 기존 연구에서는 시장의 변동을 만들어 내는 뉴스 정보 또는 거시 경제 지표를 각각 이용하거나, 몇 가지의 지표 조합만을 반영한 예측 연구가 대부분이었다. 따라서 본 연구에서는 미국 다우존스지수 예측을 위해 뉴스 정보의 감성 분석과 다양한 거시경제지표를 고려하여 효과적인 지표 조합을 제시하고자 한다. 뉴스 정보의 감성 분석은 최신 자연어처리 기법인 BERT와 NLTK VADER를 사용하고, 예측모델은 주가예측모델로 적합하다고 알려진 딥러닝 예측모델 LSTM을 적용하여 가장 효과적인 지표 조합을 제시했다.

• 한국수소및신에너지학회논문집
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• 제31권4호
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• pp.380-386
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• 2020

This paper represents an analysis of the economic impact of firing natural gas/diesel and natural gas/by-product oil mixtures in diesel engine power plants. The objects of analysis is a power plant with electricity generation capacity (300 kW). Using performance data of original diesel engines, the fuel consumption characteristics of the duel fuel engines were simulated. Then, economic assessment was carried out using the performance data and the net present value method. A special focus was given to the evaluation of fuel cost saving when firing natural gas/diesel and natural gas/by-product oil mixtures instead of the pure diesel firing case. Analyses were performed by assuming fuel price changes in the market as well as by using current prices. The analysis results showed that co-firing of natural gas/diesel and natural gas/by-product oil would provide considerable fuel cost saving, leading to meaningful economic benefits.

• East Asian Economic Review
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• 제27권3호
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• pp.213-242
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• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Korean Chemical Engineering Research
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• 제56권6호
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• pp.878-883
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• 2018

A circulating fluidized bed (CFB) has been used in various chemical industries because of good heat and mass transfer. In addition, the methanol to olefins (MTO) process requiring the CFB reactor has attracted a great deal of interest due to steep increase of oil price. To design a CFB reactor for MTO pilot process, therefore, we has examined the hydrodynamic properties of spherical catalysts with different particle size and developed a correlation equation to predict catalyst holdup in a riser of CFB reactor. The hydrodynamics of micro-spherical catalysts with average particle size of 53, 90 and 140 mm was evaluated in a $0.025m-ID{\times}4m-high$ CFB riser. We also developed a model described by a decay coefficient to predict solid hold-up distribution in the riser. The decay coefficient developed in this study could be expressed as a function of Froude number and dimensionless velocity ratio. This model could predict well the experimental data obtained from this work.

• 콘크리트학회논문집
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• 제16권1호
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• pp.102-110
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• 2004

최근 시멘트 및 골재 등 원재료 값의 상승 및 세계적인 유가 급등으로 인한 운송비의 증가로 레미콘 제조원가는 상승하고있다. 그러나 레미콘 제조업체들 간의 과당경쟁으로 인해 레미콘의 납품 단가는 오히려 낮아지고 있는 실정이다. 이를 극복하기 위한 일환으로 레미콘 제조업체들은 레미콘의 제조원가를 최소한으로 줄이고자 하는 노력 중 하나로 고로슬래그미분말 및 플라이애쉬를 혼화재로 사용하는 업체가 증가하고 있다. 그러나 이러한 광물질 혼화재를 사용한 콘크리트의 품질관리에 대한 연구는 미흡한 실정이다. 따라서, 본 연구에서는 고로슬래그미분말 혼합 콘크리트의 28일 압축강도를 조기에 예측하기 위해 열수양생법 및 표준양생에 의한 7일 압축강도를 이용하였다. 고로슬래그미분말 혼합률 별로 선형회귀분석을 실시하여 추정식을 제시하였고 90%의 신뢰구간을 나타내었다. 또한 실험의 신뢰성을 높이기 위해 모든 배합은 3회 반복하였고, 배합순서는 랜덤추출법을 사용하였다. 이러한 실험결과 열수양생법에 의한 1일 촉진강도로서 고로슬래그미분말 혼합 콘크리트의 재령 28일 압축강도를 예측할 수 있는 추정식의 신뢰성을 확인하는 성과를 얻었다.