• 산업경영시스템학회지
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• 제34권4호
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• pp.169-178
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• 2011

In this study, a forecasting engine from the user perspective is studied and developed. Characteristics of forecasting engine can be divided into a few categories, an algorithms for predicting variety of situations and the depth of algorithms based on the number and the types of data. Then applying a variety of algorithms that most closely match the predicted values for the actual value that deduce criteria for selecting an appropriate forecasting algorithm is to organize. Through the forecast quality assessment, the suggested distribution algorithm compared to the existing demand forecast algorithms is good indicators for its accuracy.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1557-1567
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• 2014

In this paper, I propose a general hypothesis that after the enactment of the Sarbanes-Oxley Act (SOA) financial statements convey more accurate and reliable corporate information to investors who in turn reflect such improvements in stock prices and test four practical hypotheses that simultaneously feature the degree of information asymmetry, forecast bias, and investor reaction to biased earnings information. The empirical results unanimously suggest that the post-SOA investors take advantage of the improvement in informational efficiency and accuracy and actively adjust for analyst forecast bias in earnings forecasts. The SOA indeed appears to achieve its primary goal of investor protection.

• 한국수자원학회논문집
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• 제39권9호
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• pp.755-766
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• 2006

중장기 기후예보는 기후역학모형의 비약적인 발전과 ENSO등의 기후현상에 대한 규명으로, 전세계적으로 정확성이 크게 향상되고 있어 중장기 유량예측의 중요한 실마리가 되고 있다. 본 연구에서는 우선 중장기 유량예측 향상을 위하여 국내에서 사용 가능한 기후정보, 즉 월간산업기상정보와 GDAPS(Global Data Assimilation and Prediction System)를 조사하고 그 정확성을 평가하였다. 월간산업기상정보와 GDAPS의 순별 예보에서 모두 초보예측보다 정확하였고 특히 갈수기보다는 홍수기에 정확성이 더 높게 나와 이 기간에는 기후예보로서 유효함을 확인하였다. 다음으로 기후예보를 이용하여 충주댐 유역에 대하여 유량예측을 수행하였다. 월간산업기상정보에서는 전체 시나리오, 교집합 시나리오, 합집합 시나리오로 나누어 유량예측에 적용하였다. 세 경우 모두 초보예측보다 평균예측점수가 높아 예측으로서 유효하였으며, 특히 홍수기에 교집합 및 합집합 시나리오의 평균예측점수가 전체 시나리오보다 높게 나타났다. GDAPS를 이용한 순별 유량예측의 경우에도 역시 갈수기보다 홍소기에 더 높은 정확성이 나타났다. 따라서 본 연구에서는 홍수기에 보다 정확한 기후예보를 사용하여 기상학적 불확실성을 줄인다면 월 유량예측의 정확성을 향상시킬 수 있음을 증명하였다.

• The Journal of Asian Finance, Economics and Business
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• 제1권3호
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• pp.29-36
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• 2014

This paper attempts to measure the impact of non-financial factors including analyst practices and broker resources on performance of sell side research. Results reveal that these non-financial factors have a measurable impact on performance of target price forecasts. Number of pages written by an analyst (surrogate for analyst practice) is significantly and directly linked with target price accuracy indicating a more elaborate analyst produces better target price forecasts. Analyst compensation (surrogate for broker resource) is significantly and inversely linked with target price accuracy. Out performance by analysts working with lower paying firms is possibly associated with motivation to migrate to higher paying broking firms. The study finds that employing more number of analysts per research report has no significant impact on target price accuracy -negative coefficient indicates that team work may not result in better target price forecasts. Though insignificant, long term forecast horizon negatively affects target price accuracy while stock volatility improves target price accuracy.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.194-194
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• 2017

Flooding is one of the most serious and frequently occurred natural disaster at many regions around the world. Especially, under the climate change impact, it is more and more increasingly trend. To reduce the flood damage, flood forecast and its accuracy analysis are required. This study is conducted to analyze the accuracy of the real-time flood forecasting of a coupled meteo-hydrological model for the Han River basin, South Korea. The LDAPS (Local Data Assimilation and Prediction System) products with the spatial resolution of 1.5km and lead time of 36 hours are extracted and used as inputs for the SURR (Sejong University Rainfall-Runoff) model. Three statistical criteria consisting of CC (Corelation Coefficient), RMSE (Root Mean Square Error) and ME (Model Efficiency) are used to evaluate the performance of this couple. The results are expected that the accuracy of the flood forecasting reduces following the increase of lead time corresponding to the accuracy reduction of LDAPS rainfall. Further study is planed to improve the accuracy of the real-time flood forecasting.

• 응용통계연구
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• 제31권2호
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• pp.229-239
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• 2018

본 논문에서는 시간적 계층 개념을 활용하여 시계열 자료를 예측하는 방법을 소개한다. 횡단적 계층 자료 분석에서와 유사한 방법으로 중복되지 않는 시간적 계층을 시계열 자료에 구조화할 수 있다. 이러한 시간적 계층을 활용하여 조정된 예측은 기존의 계층별 독립적 기저 예측 및 상향식 예측보다 더 정확하고 강건한 예측값을 생성한다. 실증 분석으로서 국내 교통사고 발생건수를 시간적 계층 개념을 활용하여 예측한다. 분석 결과, 조정 예측이 기존의 다른 예측보다 예측 성능면에서 더 우수함을 확인할 수 있다.

• 한국대기환경학회지
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• 제15권6호
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• pp.779-789
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• 1999

To support daily ground-level $O_3$ forecasting in Seoul, a transfer function model(TFM) has been developed by using surface meteorological data and pollutant data(previous-day [$O_3$] and [$NO_2$]) from 1 May to 31 August in 1997. The forecast performance of the TFM was evaluated by statistical comparison with $O_3$ concentration observed during September it is shown that correlation coefficient(R), root mean squared error(RMSE), normalized mean squared error(NMSE) and mean relative error(MRE) were 0.73, 15.64, 0.006 and 0.101, respectively. The TFM appeared to have some difficulty forecasting very high $O_3$ concentrations. To compare with this model, multiple regression model(MRM) was developed for the same period. According to statistical comparison between the TFM and MRM. two models had similar predictive capability but TFM based on $O_3$ concentration higher than 60 ppb provided more accurate forecast than MRM. It was concluded that statistical model based on TFM can be useful for improving the accuracy of local $O_3$ forecast.

• 대기
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• 제22권3호
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• pp.345-356
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• 2012

The UK Met Office Unified Model at the KMA has been operationally utilized as the next generation numerical prediction system since 2010 after it was first introduced in May, 2008. Researches need to be carried out regarding various physical processes inside the model in order to improve the predictability of the newly introduced Unified Model. We first performed a preliminary experiment for the domain ($170{\times}170$, 10 km, 38 layers) smaller than that of the operating system using the version 7.4 of the UM local model to optimize its physical processes. The result showed that about 7~8% of the improvement ratio was found at each stage by integrating four factors (u, v, th, q), and the final improvement ratio was 25%. Verification was carried out for one month of August, 2008 by applying the optimized combination to the domain identical to the operating system, and the result showed that the precipitation verification score (ETS, equitable threat score) was improved by 9%, approximately.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• 한국환경과학회지
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• 제27권7호
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.