• The Journal of the Korea Contents Association
• /
• v.15 no.10
• /
• pp.457-467
• /
• 2015

Despite the recent phenomena of Chonsei price increase, low interest rate and low growth, the indexes of financial and insurance industry production showed the results contrary to the common belief that the financial industry is sensitive to such financial crises. This is because the index of financial industry has continuously maintained a certain level of increase as opposed to the index of all industry production. Thus, this study aimed to analyze the dynamic correlation between the index of financial industry production and Chonsei price increase. A vector autoregression (VAR) model, which doesn't have a cointegrating relationship, was used to define the Chonsei price index and the indexes of all industry production and financial and insurance industry, which are macro economic variables, and describe the data. The results of the analysis on the time series data of 183 months from January 2000 to May 2015 showed that Chonsei price increase was not directly derived from the index of financial industry, but the finance industrial index affected Chonsei price increase.

• 한국방재학회:학술대회논문집
• /
• 2010.02a
• /
• pp.53.2-53.2
• /
• 2010

국내 홍수빈도해석 지침서 제공을 위한 기초 연구로서 미국 홍수빈도해석 지침서인 Bulletin 17B과 같이 국내 적합한 홍수빈도해석 기법을 제시하고자 하였다. 홍수빈도해석 지침서의 핵심은 확률분포형과 매개변수 추정법을 제시하는 것이며 이에 GEV(Generalized Extreme Value), GLO(Generalized Logistic) 분포, B-GLS(Bayesian Generalized Least Square) 기법을 대상으로 다양한 연구를 수행하였다. B-GLS 기법을 이용하여, 국내 대유역에 골고루 위치하며 댐의 영향을 받지 않는 31개 지점의 연최대 일유량 시계열의 L-변동계수(L-moment coefficient variation)와 L-왜도계수(L-moment coefficient skewness)를 추정할 수 있는 회귀모형을 제안하였다. 위 회귀모형을 구성하기 위한 유역특성으로는 유역면적, 유역경사, 유역평균강우 등을 사용하였다. Bayesian-GLS(B-GLS) 적용 결과를 OLS(Ordinary Least Square) 및 B-GLS 기법에서 지점간의 상관관계를 고려하지 않는 Bayesian-WLS(Weighted Least Square)와 비교 평가하여 그 우수성을 입증하였다. 따라서 본 연구에서 제안된 B-GLS에 의한 지역회귀모형은 국내의 미계측유역이나 또는 관측 길이가 짧은 계측유역의 홍수빈도분석을 위해 매우 유용할 것으로 기대된다. 또한 수행된 연구의 내용을 공론화하는 노력이 계속된다면 공감대가 형성된 가이드라인을 제정되는데 일조를 하리라 확신한다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.264-264
• /
• 2017

국외의 상수도 원격검침 시스템 내 데이터 전송방식은 도시 규모, 계량기의 밀도, 전력공급 여부 및 통신망의 설치 여부 등을 종합적으로 고려하여 결정되었다. 대부분의 스마트워터미터 제조업체들은 계량기의 부호기가 공급하는 판독 내용(데이터)을 전송할 검침단말기와 근거리 통신망(neighborhood area network)을 연계하여 개발 및 판매하였으며, 자체 소유 통신 프로토콜을 사용하여 라디오 주파수(RF) 통신 기술을 사용하고 있다. 광역통신망(wide area network)의 경우, 노드(말단의 계량기 및 센서)들과 이에 연결된 통신망 들을 포함한 네트웍의 배열이나 구성이 스타(star), 메쉬(mesh), 버스(bus), 나무(tree) 등의 형태로 통신망이 구성되어 있으나, 스타와 메쉬형 통신망 구성형태가 가장 널리 활용되는 것으로 조사되었다. 시스템 통합운영관리 업체들인 IBM, Oracle, Itron 등은 용수 인프라 관리 또는 통합네트워크 솔루션 등의 통합 물관리 시스템(integrated water management system)을 개발하여 현장적용을 하고 있으며, 원격검침 시스템을 통해 고객들의 현재 소비량과 과거 누적 소비량, 누수 감지 서비스 및 실시간 요금 고지 등을 실시간으로 웹 포털과 앱을 통해 제공하고 있다. 또한, 일부 제조업체들은 도시 용수공급/소비 관리자가 주민의 용수사용량을 모니터링하여 일평균 용수사용량 및 사용 경향을 파악하고, 누수를 검지하여 복구 및 용수 사용 지속가능성 지수를 제시하고, 실시간으로 주민의 용수사용량 관련 데이터를 모니터링하여 용수공급의 최적화를 위한 의사결정지원 서비스를 용수공급자에게 제공하고 있다. 최근에는 인공지능을 활용해 가정용수의 용도별(세탁용수, 화장실용수, 샤워용수, 식기세척용수 등) 사용량 곡선을 패터닝하여 profiling 기법을 도입해, 스마트워터미터에서 용수사용량이 통합되어 검지될 시 용수사용량의 세부 용도별 re-profiling 기법을 도입하여 가정용수내 과소비되는 지점을 도출 후 절감을 유도하는 기술이 개발 중이다. 또한, 미래 용수 사용량 예측을 위해 다양한 시계열 자료를 분석하는 선형 종속 모형(자기회귀모형, 자기회귀이동평균모형, 자기회귀적분이동평균모형 등)과 비선형 종속 모형(Fuzzy Logic, Neural Network, Genetic Algorithm 등)을 활용한 예측기능이 구축되어 상호 비교하여 최적의 용수사용량 예측 도구를 제공되고 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.3 no.1 s.4
• /
• pp.45-52
• /
• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

• Journal of Digital Convergence
• /
• v.16 no.10
• /
• pp.89-99
• /
• 2018

The purpose of this study is to analyse the determinants of the change in the welfare expenditure of local governments in 2015. This study analyzed the spatial correlation of welfare expenditure among neighboring local governments and determined the factors affecting the welfare expenditures. According to the results of the study, spatial correlation of welfare expenditure among local governments appears. Determinants, such as socio-economic factors, administrative factors, public financial factors are affecting the amount of the welfare expenditures, but local political factors, and local tax, last year's budgets are not correlated with the amount of local welfare expenditures. In this study, it is significant to found out that the spatial correlation of welfare expenditure among the local governments and to examine the determinants. If possible, it is necessary to analyze the time-series analysis using the multi-year welfare expenditure data, expecially self-welfare expenditures.

• Korean Journal of Construction Engineering and Management
• /
• v.13 no.2
• /
• pp.128-136
• /
• 2012

In this study, we investigated the macroeconomic variables that affect housing prices thus creating a large impact on people's lives as well as the real estate market. For the study, the macroeconomic variables able to influence the House Rental Price (housing price by lease or deposit) were used for an analysis as follows: housing sales price index, household loans rate, total household savings, the number of employees and a multiple regression analysis was performed using a time series for each macroeconomic variable. As a result of the analysis, the House Rental Price was affected by all of four macroeconomic variables. The House Rental Price increased as each variable enlarged. In conclusion, this study may be useful for finding a solution for stabilizing the House Rental Price as well as for the establishment of efficient and sustainable policies for the housing market.

• International Area Studies Review
• /
• v.16 no.3
• /
• pp.323-341
• /
• 2012

In the recent year, the urbanization is emerging as important issue for sustainable development in China. Like the most of the world, urbanization of China is closely related with the domestic market development, the innovation of industrial structure, and the reduction of income cap among regions, urban-rural region and so on. This paper analyzes the impact of urbanization on economic growth using cross section data and time series data of the eastern coastal regions in China. Based on the existing literature, we establish a hypothesis, which is basically the same as Williamson(1965)'s hypothesis, that urbanization promotes the economic growth at the early stages of development but has adverse effects in economies that have reached a certain income level. The results of study are as follows: Using 10-provinces data of the eastern coastal region in China, this paper examines the impact of urbanization on economic growth. Regression results suggest that Williamson's hypothesis is not verified, regardless of estimation methods in two models. Hence, the results show that the impact of urbanization on economic growth has not the inverse U-type function in the eastern coastal region of China.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.6 no.4
• /
• pp.43-52
• /
• 1986

With increasing of computer use, a least squares method is now widely used in the regression analysis of various data. Unreliable results of regression coefficients due to the floating point of computer and problems of ordinary least squares method are described in detail. To improve these problems, a least squares method using orthogonal function is developed. Also, Comparison and analysis are performed through an example of numerical test, and re-orthogonalization method is used to increase the accuracy. As an example of application, the optimum order of AR process for the time series of monthly flow at the Pyungchang station is determined using Akaike's FPE(Final Prediction Error) which decides optimum degree of AR process. The result shows the AR(2) process is optimum to the series at the station.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.255-263
• /
• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.305-309
• /
• 2002

소지역에서 직접(direct) 시계열추정을 할 수 있다면, 소지역들 추정에서 최적선형 불편예측량(BLUP)을 일반화 시킬 수 있다. 특히 조사에서 얻어지는 관측 값의 오차가 시간상으로 상관관계가 있다면 Kalman-Filter(K-F)기법이 사용 될 수 있다. 이 연구는 소지역의 실업자 수 추정에서 K-F기법으로 경제활동인구수를 이용하여 현 시점의 소지역 실업자 수를 예측함수(BLUP)를 통해 추정하였다. 그리고 단순 회귀분석 추정치와 비교하였다.