• 한국건설관리학회논문집
• /
• 제14권4호
• /
• pp.24-34
• /
• 2013

2004년부터 정부는 무분별한 저가입찰을 방지하고, 기술 경쟁에 의한 적정 시장 가격 반영 및 효율적인 계약관련 업무를 추진하는 것을 목적으로 실적공사비 제도를 도입 시행하고 있다. 하지만 실적공사비 제도의 도입이 낙찰단가 하락에 의한 정부의 예산 절감에만 기여할 뿐, 실질적인 시장가격을 반영하고 있지 못하고 있다는 우려의 목소리 또한 꾸준히 제기되고 있는 실정이다. 낙찰단가 하락에 의한 일반건설업체의 비용 부담은 전문건설업체로 전가되며 최종적으로 건설노동자의 피해로 이어질 가능성이 크기에, 실적공사비에 적정 가격을 반영하고 현실화하는 것은 성공적인 실적공사비 제도의 정착에 매우 중요한 요소이다. 따라서 본 연구는 노무비를 중심으로 노무중심공정을 도출하고 이들의 실적공사비단가와 해당 기능공의 시중노임단가를 비교하여 실적공사비의 현실화수준을 파악하고, 시계열분석을 통해 변화를 분석하고 예측하였다. 시장가격이 반영되지 않은 낙찰 단가의 실질적 하락은 노무 환경의 변화를 가속화하고, 임금체불, 업체부도 등 건설근로자의 직접적인 피해로 이어질 수 있기에 향후 본 연구가 현행 실적공사비 제도의 문제점을 해결하고, 개선방안을 수립하기 위한 기초 자료로 활용될 수 있을 것으로 기대된다.

• 한국산림과학회지
• /
• 제50권1호
• /
• pp.29-35
• /
• 1980

기존(旣存)의 시계열자료(時系列資料)를 회귀분석(回歸分析)함으로써 우리나라 목재수요(木材需要)를 장기예측(長期予測)하였다. 이론적(理論的)인 검토(檢討)를 거쳐 모집(募集)된 자료(資料)는, 계량분석(計量分析)이 가능하도록 정리(整理)해서 가상적(假想的)인 수요함수(需要函數)를 도출(導出)하였다. 설명변수(説明変数)는 수요산업(需要産業)의 생산활동(生産活動)과 상대가격(相対価格)을 택했으며 모형(模型)의 예측력(予測力)을 검증(検證)한 후, 5 차(次) 5 개년계획(個年計㓰)(안(案))의 지침자료(指針資料)에 의해 총량예측치(總量予測値)를 추정(推定)하였다. 이러한 Simulation 과정(過程)을 거쳐 추정(推定)된 장기국내수요(長期國內需要)는 1987년(年)에 8,480천(千)$m^3$, 1991년(年)에는 10,670천(千)$m^3$로 증가(増加)될 것으로 전망(展望)되었으며 총수요(總需要)는 1987년(年)에 21,430천(千)$m^3$ 그리고 1991년(年)에는 27,190천(千)$m^3$가 수요(需要)될 것으로 추계(推計)되었다.

• Journal of Korea Trade
• /
• 제24권1호
• /
• pp.131-145
• /
• 2020

Purpose - The recent growth of South Korean products in the international market is the benchmark for both developed as well as developing countries. According to the development index, the role of international trade is indeed crucial for the development of the national economy. However, the visualization of the international trade profile of the country is the prerequisite of governmental policy decision-makers and guidance for forecasting of foreign trade. Design/methodology - We have utilized data visualization techniques in order to visualize the import & export product space and trade partners of South Korea. Economic Complexity Index (ECI) and Revealed Comparative Advantage (RCA) were used to identify the Korean international trade diversification, whereas the time series approach is used to forecast the economy and foreign trade variables. Findings - Our results show that Chine, U.S, Vietnam, Hong Kong, and Japan are the leading trade partners of Korea. Overall, the ECI of South Korea is growing significantly as compared to China, Hong Kong, and other developed countries of the world. The expected values of total import and export volume of South Korea are approximately US$535.21 and US$ 781.23B, with the balance of trade US$ 254.02B in 2025. It was also observed from our analysis that imports & exports are equally substantial to the GDP of Korea and have a significant correlation with GDP, GDP per capita, and ECI. Originality/value - To maintain the growth rate of international trade and efficient competitor for the trade partners, we have visualized the South Korea trade profile, which provides the information of significant export and import products as well as main trade partners and forecasting.

• 한국데이타베이스학회:학술대회논문집
• /
• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 대기
• /
• 제24권1호
• /
• pp.29-38
• /
• 2014

A characteristic of snowfall and minimum temperature variability in South Korea with respect to the variability of Arctic Oscillation (AO) was investigated. The climatic snowfall regions of South Korea based on daily new fresh snowfall data of 59 Korea Meteorological Administration (KMA) stations data corresponding to the sign of AO index during December to February 1979~2011 were classified. Especially, the differences between snowfalls of eastern regions and that of western regions in South Korea were seen by each mean 1000hPa geopotential height fields, which is one of physical structure, for the selected cases over the East Asia including the Korean Peninsula. Daily minimum temperature variability of 59 KMA station data and daily AO index during the same period were investigated using Cyclo-stationary empirical orthogonal function (CSEOF) analysis. The first CSEOF of wintertime daily AO index and that of minimum temperature of 59 KMA stations explain 33% and 66% of total variability, respectively. Correlation between principal component time series corresponding to the first CSEOF of AO index and that of temperature at the period of 1990s is over about -0.7 when that of AO index leads about 40 days.

• Journal of the Korean Data and Information Science Society
• /
• 제26권2호
• /
• pp.289-299
• /
• 2015

본 연구는 PM10 농도에 대한 계량치 예측모형 개발을 목적으로 한다. 세 종류의 자료 (기상관측 자료, 세계기상통신망 중국 관측자료, 대기질 화학수치모델자료)를 예측인자로 사용하였으며, 일일 단기예보 시스템에 쉽게 적용할 수 있도록 시간자료를 일자료로 변환하였고 시차변환을 수행하였다. 상관분석과 다중공선성 진단을 통하여 예측인자를 선택하고 두 종류의 모형 (중회귀모형, 문턱치 회귀모형)을 각각 적합하였다. 모형 안정성 검사를 위하여 모형검증을 수행하였으며, 전체자료를 사용하여 모형을 재추정한 후 예측치와 관측치 사이의 산점도와 시계열그림, RMSE, 예측성 평가측도를 작성 및 산출하여 두 모형을 비교하였다. 문턱치 회귀모형의 예측력이 고농도 PM10예측에서 다소 우수한 결과를 보였다.

• International Journal of Control, Automation, and Systems
• /
• 제6권5호
• /
• pp.639-650
• /
• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• 한국지반공학회지:지반
• /
• 제9권3호
• /
• pp.67-76
• /
• 1993

우리나라는 많은 산막지역으로이루어져 있으며 우기에 많은산사태의 발생으로 인하여 인명과 재산의 손실을 입고 있다. 따라서, 산사태의 발생에 대한 예측 시스템과 위험도 분석 연구가 필요하며, 본 연구의 목적은 관측된 지하수위의 분석을 통하여 산사태 발생을 예측하는 가능성에 대한 것이다. 이를 위하여 AR 모델을 사용하여 모델계수를 일정하게 하는 경우와 변화시키는 경우로 나누어 분석하였다. AR모델계수를 일정하게 하는 경우에는 AR(1), AR(2), AR(3) 모델을 선택하여 각 각의 모델계수를 구하였고, AR모델계수를 변화시키는 경우에는 변형된 AR(1)과 전형적인 AR (2) 모델을 과정 모델로 이용하여 Kalman Filtering 기법에 의하여 모델계수를 구하였다. 그 결과, 모델계수를 변화시키는 실시간 예측 방법이나 AR모델계수가 일정한 경우 모두 산사면 에서의 지하수위를 잘 예측해주며, 지하수위 뿐만아니라 시간별 강우강도를 고려함으로써 더욱 정 확한 예측을 할 수 있을 것으로 사료된다.

• 응용통계연구
• /
• 제20권3호
• /
• pp.541-550
• /
• 2007

시계열자료를 분석하는데 있어 중요한 목적 중에 하나가 미래값에 대한 예측이다. 일반적으로 자기회귀이동평균모형에서는 백색잡음이 정규분포를 따른다는 가정 하에서 모수의 추론과 예측 및 예측구간의 추정이 이루어지고 있다. 그러나 자료가 이러한 가정을 만족하지 않는 경우, 자료를 가정에 맞게 변환시킨 후 분석하는 방법을 생각해 볼 수 있다. 이 논문에서는 변환된 자료를 분석하여 얻은 결과를 이용하여 본래의 척도에서의 미래값에 대한 예측구간을 추정하는 문제에 대해 알아본다. 제안하는 방법에서는 먼저 적절한 변환을 이용하여 자료를 정규가정을 만족하도록 변환시키고 변환된 자료를 이용하여 미래값에 대한 예측구간을 추정한 후, 역변환을 이용하여 예측구간을 추정한다. 이 논문에서는 시계열분석에서 모델링이 상대적으로 어려운 왜도의 문제를 해결하기 위해 Yeo-Johnson 변환을 중심으로 한 방법론을 소개한다. 모의실험 결과 제안된 방법에 의한 단측예측구간의 포함확률이 변환을 사용하지 않은 구간보다 명목수준에 가까운 것을 확인하였다.