• Journal of Information Processing Systems
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• 제12권1호
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• 전기학회논문지
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• 제63권3호
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• pp.383-388
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• 2014

This paper describes a wave modeling method using low-cost sensors. Wave modeling is applied to the wave monitoring system for accurate measurement of ocean wave parameters. The observation of ocean wave parameters is necessary to improve the accuracy of forecast of ocean wave condition. However, the ocean wave parameters measured by a low-cost wave monitoring system suffer from several errors. Therefore we introduce a wave modeling method to compensate the ocean wave parameters corrupted by errors. The proposed method is analyzed using experiments within controlled environment. It is verified that the accuracy of low-cost wave monitoring system can be increased by the proposed method.

• 대한산업공학회지
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• 제7권1호
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• pp.21-31
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• 1981

This paper develops a short term forecasting model for household electric power consumption in Seoul, which can be used for the effective planning and control of utility management. The model developed is based on exponentially weighted moving average model and incorporates monthly average temperature as an exogeneous factor so as to enhance its forecasting accuracy. The model is empirically compared with the Winters' three parameter model which is widely used in practice and the Box-Jenkins model known to be one of the most accurate short term forecasting techniques. The result indicates that the developed hybrid exponential model is better in terms of accuracy measured by average forecast error, mean squared error, and autocorrelated error.

• 한국시스템다이내믹스연구
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• 제9권1호
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• 경영과정보연구
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• 제36권3호
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• pp.151-165
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• 2017

본 연구는 수주산업 기업의 진행기준 수익인식 방법이 재무분석가 이익 예측 정보에 미치는 영향을 분석한다. 구체적으로 미청구공사 계정 잔액 보고여부 및 잔액 수준에 따라 달라지는 재무분석가 이익예측 정보의 특성을 살펴보았다. 미청구공사 계정 정보는 K-IFRS 도입 이후부터 제공되고 있으므로, 본 연구는 2010년부터 2014년까지 한국거래소에 상장된 기업 중 수주산업에 속한 453개 기업-연도 표본을 대상으로 분석하였다. 분석결과, 미청구공사 계정 잔액이 존재하는 기업은 미청구공사 계정 잔액이 없는 기업에 비해 재무분석가 이익예측 정보의 정확성이 낮았고, 더불어 미청구공사 보고금액의 수준이 높아질 경우 재무분석가 이익예측 정보의 정확성이 감소됨을 확인하였다. 미청구공사 계정은 수주업체 수익(진행률) 인식이 발주자의 수익 인정 시점(실제진척도) 보다 먼저 인식될 경우 생성된다. 이는 진행률 측정 시 경영자의 재량적인 판단과 추정이 가능하기 때문이며, 결국 실제진척도와 진행률의 차이는 재무제표의 예측가치를 하락시킨다. 따라서 진행기준에 의한 수익인식 방법을 적용 시 미청구공사 잔액을 보고한 기업의 경우, 재무분석가의 이익예측은 보다 어려울 수 있음을 본 연구의 결과가 보여준다. 추가적으로 미청구공사 계정잔액을 보고한 기업은 재무분석가의 이익예측 성향이 낙관적인 것으로 나타났다. 본 연구는 경영자에게는 실제진척도를 반영할 수 있는 진행률 측정 방식의 도입과 더불어 진행률 측정시에 자의적인 조정과 추정을 줄이는 노력을 제안하며, 투자자들에게는 수주산업의 진행기준 회계처리의 특성을 감안한 투자와 분석을 권고한다. 아울러 본 연구의 결과는 정책당국의 수주산업 회계투명성 제고 방안에도 힘을 실어 준다.

• 한국환경과학회지
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• 제20권9호
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• pp.1095-1104
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• 2011

A survey of professionals employed in marine related fields was conducted on subjects related to marine meteorological forecasts and special reports. The outcome of the survey indicated that the respondents were overall satisfied with the determination of the zones related to marine meteorological forecasts and special reports and with the number of forecast factors, but in regards to the questions about specific adjustment methods, it was found that the respondents perceived a need for adjustment. In addition, although there was a high consensus among the respondents that the criteria for watch and warning in the marine special reports were suitable, they voiced the opinion that it will be necessary to implement changes in the current criteria for watch and warning in order to further improve the compatibility of the criteria. The survey found that there was a high level of utilization for the marine meteorological information provided by the Korea Meteorological Administration(KMA), and that respondents mostly acquired this information via internet and TV. On the other hand, however, the satisfaction level regarding the accuracy of the marine meteorological information was low in comparison to the utilization level. The survey regarding areas for improvement in the forecasts and special reports also indicated that the need for 'improvement in the accuracy of forecasts' was cited most frequently.

• 한국환경과학회지
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• 제18권2호
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• pp.129-139
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• 2009

Development of an artificial neural network model was presented to predict the daily maximum $SO_2$ concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using $SO_2$ potential parameters estimated from meteorological and air quality data which are closely related to daily maximum $SO_2$ concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the $SO_2$ potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high $SO_2$ concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum $SO_2$ at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum $SO_2$ concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.

• 한국융합학회논문지
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• 제8권11호
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• pp.363-372
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• 2017

본 연구는 경영자가 제공하는 이익예측정보가 미래 이익의 질에 어떤 영향을 미치는지 분석하였다. 연구의 분석기간은 관심변수(종속변수)를 기준으로 하여 2003년부터 2009년까지(2004년부터 2011년까지)이며, 유가증권상장기업 중 경영자가 영업이익의 예측치를 공시한 기업을 대상으로 총 475개 기업/년 자료가 분석에 이용되었다. 분석결과를 살펴보면 첫째, 당기 경영자의 이익예측성향이 낙관적일수록 미래 이익의 질이 낮은 것으로 나타났다. 둘째, 당기 경영자의 이익예측정확성이 낮을수록 미래 이익의 질이 낮은 것으로 나타났다. 이러한 결과는 미래이익의 질을 결정하는데 있어 경영자의 이익예측정보가 활용될 수 있음을 시사한다.