• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.747-757
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• 2021

The primary purpose of the study is to investigate the volatility spillover from financial uncertainty (FU) of the United States (US) to the stock markets of SAARC member countries including India, Sri-Lanka, Pakistan, and Bangladesh. The empirical literature overlooked SAARC countries and the FU index. Based on the estimation method, the data of FU is available for three different forecast horizons including 1-month, 3-months, and 12-months. For empirical analysis, monthly data is used from February 2013 to September 2019. EGARCH model is employed to investigate the volatility spillover effects. The findings of the study show that the spillover effect of FU varies with the forecast horizon. The FU with a higher forecast horizon has a significant spillover effect on more countries. The spillover effect of US financial uncertainty is negative in most of the SAARC countries. Bangladesh stock market is influenced by FU with all three forecast horizons whereas the volatility of the Pakistan stock market is not influenced by FU with any forecast horizon. The findings are consistent with the concept of "limited trade openness" in the financial markets of emerging economies. The emerging economies avoid financial market openness to minimize the risk of spillover of other countries.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2006년도 춘계종합학술대회
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• pp.1039-1042
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• 2006

최근 들어 고객정보의 체계적인 분석과 고객의 다양한 패턴을 발견하고 분석 및 예측을 하기 위한 목적으로 많은 기업들이 eCRM을 빠르게 도입하고 있고 과거에 주로 사용되던 통계적 과정을 자동화하여 일반인들도 쉽게 양질의 결과를 추출하고 예측 할 수 있는 데이터마이닝으로 점점 대체되고 있는 추세이다. 이러한 데이터마이닝이 대표적으로 이용되고 있는 분야가 eCRM이다. 본 논문에서는 A할인점의 고객 데이터와 1년간의 매출 데이터를 기반으로 데이터마이닝을 동해 이듬해 고객기여도를 예측하는 실험을 하여 실제 데이터와 예측된 데이터와의 비교를 통해 데이터마이닝이 eCRM에 얼마나 효과적인지를 입증하였다.

• 한국항만경제학회지
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• 제32권4호
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• pp.1-13
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• 2016

본 연구는 System Dynamics를 이용하여 선사 컨테이너 인벤토리의 수요를 장비 Type/size별 예측, Port별 예측, Weekly 예측을 통해 보다 정교한 예측모델을 구축하는 것을 연구의 목적으로 하였다. 예측은 중국의 상하이항과 얀티안항을 대상으로 하였다. 컨테이너 인벤토리는 수요가 많고 유효한 데이터를 산출할 수 있는 Dry 컨테이너 20', 40', High cube 40'으로 한정하였다. 시뮬레이션 기간은 2011년-2017년이며, 선사에서 실제 예측하는 단위인 Weekly 데이터를 활용하였다. 모델의 정확도 검증을 위해 절대비율 평균오차(MAPE)를 적용한 결과 상하이 Dry 40' 수요, 상하이 Dry High cube 40' 수요, 상하이 Dry 20' 공급, 상하이 Dry 40' 공급, 상하이 Dry High cube 40' 공급 예측 모델은 $$0%{\leq_-}MAPE{\leq_-}10%$$에 속하는 매우 정확한 예측 모델로 검증되었다. 그 외의 상하이 수요 공급 예측 모델은 $$10%{\leq_-}MAPE{\leq_-}20%$$에 속해 비교적 정확한 예측 모델로 검증되었다. 얀티안 Dry High cube 40' 수요, Dry 20' 공급 예측 모델은 $$0%{\leq_-}MAPE{\leq_-}10%$$에 속해 매우 정확한 예측 모델이며, 그 외의 얀티안 수요 공급 예측 모델은 $$10%{\leq_-}MAPE{\leq_-}20%$$에 속해 비교적 정확한 예측 모델로 검증되었다. 본 연구의 예측 모델은 실제 선사에서 관리중인 데이터와 비교해도 높은 정확도를 갖는 것으로 나타났다. 본 연구에서 제시된 모델은 지역 수요예측 담당자 및 본부의 인벤토리 컨트롤 담당자가 참고자료로 유용하게 사용 가능하다.

• The Plant Pathology Journal
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• 제36권1호
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• 대기
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• 제15권4호
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• pp.229-239
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• 2005

The effect of inversion layer on the land and sea breeze near the Gangneung city was investigated. The land and sea breeze occurrence days were selected, and the height and the intensity of inversion layer were calculated with the upper air observational data of the Sokcho Station. The relationships between the temperature variation near the Gangneung and the inflow time, inland penetration and the inflow depth of the land and sea breeze were also analyzed. And the Gangwon Short-range prediction system was verified with the comparison of surface stream line by the Gangwon short-range prediction system with the AWS wind vector data. It was revealed that the inversion layer tended to block the sea breeze, shorten the inland penetration distance and lower the inflow depth, causing the temperature rise. The comparison and analysis of surface steam line by the Gangwon short-range prediction system and the AWS wind vector showed that the system quite well simulated the sea breeze, thus the system could be well utilized in the prediction of land and sea breeze.

• 대기
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• 제21권2호
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• 경영과학
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• 제26권1호
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• 유통과학연구
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• 제20권12호
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• pp.117-123
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• 2022

Purpose: This study investigates the relationship between earnings forecasts estimated from a cross-sectional earnings forecast model and firm characteristics such as firm size, sales volatility, and earnings volatility. Research design, data and methodology: The association between earnings forecasts and the aforementioned firm characteristics is examined using 214 firm-year observations with analyst following and 848 firm-year observations without analyst following for the period of 2011-2019. I estimate future earnings using a cross-sectional earnings forecast model, and then compare these model-based earnings forecasts with analysts' earnings forecasts in terms of forecast bias and forecast accuracy. The earnings forecast bias and accuracy are regressed on firm size, sales volatility, and earnings volatility. Results: For a sample with analyst following, I find that the model-based earnings forecasts are more accurate as the firm size is larger, whereas the analysts' earnings forecasts are less biased and more accurate as the firm size is larger. However, for a sample without analyst following, I find that the model-based earnings forecasts are more pessimistic and less accurate as firms' past earnings are more volatile. Conclusions: Although model-based earnings forecasts are useful for evaluating firms without analyst following, their accuracy depends on the firms' earnings volatility.

• 응용통계연구
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• 제35권5호
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• pp.645-655
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• 2022

본 논문에서는 R에서 시계열 자료 예측을 위한 자동화 함수에 대하여 고찰하고 그 예측 성능을 비교합니다. 대표적인 시계열 예측 방법인 지수 평활 모형과 ARIMA (autoregressive integrated moving average) 모형을 대상으로 하였으며, 이들의 모형화 및 예측 자동화를 가능하게 하는 R의 4가지 자동화 함수인 forecast::ets(), forecast::auto.arima(), smooth::es()와 smooth::auto.ssarima()를 대상으로 하였습니다. 이들의 예측 성능을 비교하기 위하여 3,003가지의 시계열로 구성되어 있는 M3-Competition자료와 3가지의 정확성 척도를 사용하였습니다. 4가지 자동화 함수는 모형화의 다양성 및 편리성, 예측 정확도 및 실행 시간 등에서 각자 장단점이 있음을 확인하였습니다.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.559-564
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• 2013

In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.