Earticle

연구 목적: 본 연구 목적은 전라남도의 대중골프장과 회원골프장의 이용객 수요를 연령별, 골프 경력별, 골프 핸디캡별로 차이를 예측하고자 한다. 또한 소비가치가 재이용의도에 영향이 있는지를 살펴보고, 대중골프장과 회원골프장의 증가가 매개효과가 있는지를 살펴보고자 한다. 연구 방법: 설문조사는 2022년 9월 1일부터 10월 30일까지 273명을 대상으로 하였다. 연구 내용: 첫째, 이용객의 소비가치가 재이용의도에 미치는 영향을 제시하였다. 둘째, 대중골프장과 회원골프장의 증가가 소비가치에 매개효과가 있다는 것을 제시하였다. 셋째, 대중골프장과 회원골프장의 증가를 연령별, 골프 경력별, 골프 핸디캡별로 분류하여 차이를 제시하였다. 결론 및 제언: 전라남도 골프장 산업이 수요중심의 주력산업으로 성장할 수 있는 기초자료를 제공하고, 마케팅적 접근을 통한 전라남도의 골프 산업이 활성화되길 기대한다.

The purpose of this study is to predict the difference in customer demand between public golf courses and member golf courses in Jeollanam-do by age, golf experience, and golf handicap. In addition, we will examine whether consumption value has an effect on re-use intention, and whether the increase in public golf courses and member golf courses has a mediating effect. The survey was conducted on 273 people from September 1, 2022 to October 30, 2022. First, the effect of consumption value of users on re-use intention was presented. Second, it was suggested that the increase in public golf courses and member golf courses has a mediating effect on consumption value. Third, the increase in public golf courses and member golf courses was classified by age, golf experience, and golf handicap, and differences were presented. It is expected that the golf industry of Jeollanam-do will be revitalized through a marketing approach and provide basic data for the golf course industry of Jeollanam-do to grow into a demand-oriented flagship industry.

Objectives: Main objective of this study was to estimate crematory facility demand of Suwon city, using Seasonal ARIMA Model. This study aims to estimate the demand based on the number of currently operating crematory facilities, dividing into inward district and outward district of Suwon city. Methods: As the construction of crematory facilities is greatly in need of supply along with the increasing funerals, it requests more accurate estimation of the demand in specific area. This study employed Seasonal ARIMA Model which is useful to deal with time series data with small size and various patterns, instead of the Gompertz curve and logistic curve frequently used in the past. This study did analyses in four steps, discrimination of stationarity (balance for average and dispersion), identification of functions (ACF, PACF), model diagnostic (estimation of parameter), and decision (forecasting of crematory facilities). Results: First, the demand of cremation in inward district of Suwon was estimated to be 4,051 persons in 2017 and to be 5,129 persons in 2022 using the ARIMA (0,1,1)(1,1,0)S=12 Model, which is to be increased 31.8 percent points compared to that of 2016. Second, the demand of cremation in outward district of Suwon was estimated to be 6,731 persons in 2017 and 7,060 persons in 2022 using the ARIMA (0,0,3)(1,1,1)S=12 Model. Altogether the users of crematory facilities were estimated to be 10,782 persons in 2017 and 12,189 persons in 2022, which shows increasing trend of 3 to 4 percent points every year. Conclusions: This study proved that Seasonal ARIMA Model is a proper tools to estimate the crematory facility demand in specific area. Demand and supply of crematory facilities should be estimated based on reliable statistics and data. Suwon city should provide more facilities to meet the increasing need of cremation. For this, Suwon city should input more investment for improving of quality of facilities and coordination of the number.

Korean housing market currently faces demographic changes such as the increase in single-person or elderly households and the deceleration of population growth. Based on these changes some researchers anticipate that the demand for small-sized housing units will dramatically increase. And they argue that the portion of small-sized units in housing construction has to be increased. We propose the use of quantile regression as a method for estimating the distributional change of dwelling sizes and forecast long-term housing demand. The advantage of using quantile regression is that it can produce estimates of changes in the distribution of a dependent variable. In light of this issue, the current study examines changes in the distribution of dwelling sizes in the Korean housing market during the past 35 years and estimates housing demand change based on existing trends. The results show that both the average size of housing units and the range of dwelling sizes have increased in the last 35 years. Quantile regression suggests that the estimated future housing demand suggests that demand for medium and large-sized units will increase despite the upward trend of one- or two- person households. These results imply that the demand for small housing units will not be dominant and the excessive construction of small-sized units should be avoided.

‘동태적 정보취득가설’의 검증에 있어서 ‘사적정보 지표’의 존재여부가 결정적으로 중요함에도 불구하고, 국내외를 막론하고 사적정보 지표를 직접 활용하여 실증연구를 진행한 기존연구는 찾기 어렵다. 수요예측과정에서 주간사가 투자자로부터 수집하는 ‘사적정보 지표’를 관측하는 것이 어렵기 때문이다. 때문에 대부분의 실증연구는 수요예측기간 중 공모가조정률 자체를 사적정보의 대용치로 사용하여 왔다. 이 논문은 참여자들이 제시한 가격의 물량가중평균, 최종공모가 이상 참여경쟁률 등의 자료를 수집하여 수요예측과정에서 생산된 정보의 지표자료를 구축하고, 이들 지표를 사용하여 우리나라 수요예측과정의 정보생산기능과 보상체계로서의 기능을 실증 분석하였다. 분석 결과 수요예측기간 중 공모가조정률은 수요예측과정에서 얻어진 정보와 통계적으로 유의한 관계를 지닌 것으로 나타났다. 그러나 신규공모주 수익률에 대한 설명력은 불분명하였다. 주간사의 재량권이 확대된 최근 기간의 경우, 수요예측기간 중 공모가조정률과 신규공모주 수익률 모두에 대해 통제변수의 포함여부와 무관하게 강건한 통계적 유의성을 보인 변수는 공적 정보변수였다. 이 같은 실증결과는 ‘동태적 정보취득가설’의 한국 신규공모시장에 대한 정합성에 의문을 제기한다.

We examine if the book-building procedure at the KOSDAQ market extracts private information from informed investors as argued by the dynamic information acquisition hypothesis a la Benveniste and Spindt (1989). Although, to test the hypothesis, it is desirable to have a direct measure of private information, most of previous studies have used price adjustment during book-building as a proxy for private information. In this study, using the unique data of the KOSDAQ equity issuing market, we construct direct measures of private information provided by institutional investors under the book-building procedure, which include the average bidding price and the oversubscription ratio. We collect each bid's information from book and measure limit prices by their quantity-weighted average and oversubscription at the issue price. We characterize these measures as private information provided during book-building, and analyze the effects of private information on issue price and whether the effects can be explained by the dynamic information acquisition hypothesis, namely a mechanism of information providing and compensation in KOSDAQ equity issuing market. We find that our measures of private information during book-building affect significantly the final IPO price. However, private information in bids has a vague and insignificant effect for the abnormal returns around the IPOs. In particular, during period 3 when Korean underwriters have a wide discretionary authority, public information, which is the KOSDA index return during book-building, affects significantly both the issue price adjustment during book-building and the abnormal returns around the IPOs. Consequently, our results do not support the dynamic information acquisition hypothesis at the KOSDAQ market.

Oriental medical tourism is emerging as an alternative tourism market in Korea. The primary purpose of this study was to forecast the demand for oriental medical tourism in Korea using both quantitative and qualitative methods. To this end, total population of Korea in 2010 was predicted using a cohort component method (www.nso.go.kr). Then, willingness to participate in oriental medical tourism was calculated from the survey on five metropolitan residents in Korea. The secondary purpose of this study was to estimate the economic impact of oriental medical tourism on the Korean economy using an input-output(I-O) model. Results indicate that demand for oriental medical tourism was predicted to be 4.4 million for 6 metropolitan cities and 10 million across the country. Also, the results of the I-O analysis show that oriental medical tourism generated 5,238 billion Won of output impact, 933 billion Won of income impact, 2,651 billion Won of value-added impact, and 69,709 jobs.

We consider the application of the Bayesian inference approach to demand parameter estimation, implementing it with WinBUGS® software based on airline seat booking data. Specifically, this research presents a statistical analysis of the real data using Markov Chain Monte Carlo (MCMC) techniques. Markov Chain Monte Carlo method is usually used to obtain estimates of posterior distribution. The non-homogeneous Poisson process (NHPP) model with gamma priors is used and several variant models are developed. Finally, computation results are presented and compared.

The purpose of this study is to provide cybershopping malls of electronic market our country with the suggestive point in the Demand forecasts for coping with our continuously-changing environment on the basis of data gained through the following results of my research work. We tried the forecast on the demand to analyze the proper amount of supply. we did the analysis by using the statistic program of Box-Jenkins in SPSSWIN Version 10.0 for 13 months from November 2000 to November 2001, various issues of sales with this analysis are as follows. First, the data on the difference of sales has showed that is a difference between the sales of demand. Second, as in shown in the analysis result of demand forecast, clothing, books, sport goods...showed the increasing trend.

본 연구의 목적은 외부경제요인을 고려할 수 있는 새로운 산업용지 수요예측 방법을 제시하는 것이다. 분석모형은 외생변수를 고려할 수 있는 ARIMA-X를 이용하였다. 외생변수는 경제 및 산업구조를 반영할 수 있도록 거시경제, 제조업 경기실사지수 및 경기종합지수 변수들로 구성된다. 그리고 예측은 외생변수 중 산업용지 공급보다 선행하는 변수만을 사용한다. 산업용지 공급에 선행성을 갖는 변수는 수입액, 민간·정부소비지출, 총자본형성, 경제심리지수, 기계류내수출하지수, 경기종합선행지수로 나타났다. 이들 변수를 이용하여 ARIMA-X 모형을 추정한 결과, 수입액 변수만 포함된 ARIMA-X(1,1,0) 모형이 통계적으로 유의한 것으로 나타났다. 산업용지 수요예측은 수입액의 변화 시나리오를 반영하여 2021년부터 2030년까지의 산업용지를 예측하였다. 그 결과, 장래 산업용지 수요는 연평균 1.91% 증가한 1,030.79 km²로 예측되었다. 이 결과를 기존 지수평활법과 비교한 결과, 본 연구의 결과가 기존 모형보다 예측오차가 더 적게 나타났다. 새로운 산업용지 예측모형으로 사용가능할 것으로 기대된다.

The purpose of this study is to present a new industrial land demand prediction method that can consider external economic factors. The analysis model used ARIMA-X, which can consider exogenous variables. Exogenous variables are composed of macroeconomic variable, Business Survey Index, and Composite Economic Index variables to reflect the economic and industrial structure. And, among the exogenous variables, only variables that precede the supply of industrial land are used for prediction. Variables with precedence in the supply of industrial land were found to be import, private and government consumption expenditure, total capital formation, economic sentiment index, producer's shipment index, machinery for domestic demand and composite leading index. As a result of estimating the ARIMA-X model using these variables, the ARIMA-X(1,1,0) model including only the import was found to be statistically significant. The industrial land demand forecast predicted the industrial land from 2021 to 2030 by reflecting the scenario of change in import. As a result, the future demand for industrial land was predicted to increase by 1.91% annually to 1,030.79 km². As a result of comparing these results with the existing exponential smoothing method, the results of this study were found to be more suitable than the existing models. It is expected to b available as a new industrial land forecasting model.

The purpose of this study is to provide meaningful information for various stakeholders' decision-making process through forecasting of domestic beef demand. Three different exponential smoothing models were evaluated, and a double exponential smoothing model was used to forecast domestic beef demand based on time-series data, As a result of the forecast, domestic beef consumption is expected to increase by 37,000 to 40,000 tons per year from 2020 to 2025.

2020년 1월 국내에 첫 코로나19 확진자가 발생한 후 버스와 지하철 같은 대중교통이 아닌 공공자전거와 같은 개인형 이동수단에 대한 관심이 증가하였다. 서울시에서 운영하는 공공자전거인 '따릉이'에 대한 수요 역시 증가하였다. 본 연구에서는 서울시 공공자전거의 최근 3년간(2019~2021) 시간대별 대여이력을 바탕으로 게이트 순환 유닛(GRU, Gated Recurrent Unit)의 수요예측 모델을 제시하였다. 본 연구에서 제시하는 GRU 방법의 유용성은 서울시 영등포구 여의도에 위치한 여의나루 1번 출구의 대여이력을 바탕으로 검증하였다. 특히, 동일한 조건에서 다중선형회귀 모델 및 순환신경망 모델들과 이를 비교 분석하였다. 아울러, 모델 개발시 기상요소 이외에 서울시 생활인구를 변수로 활용하여 이에 대한 검증도 함께 진행하였다. 모델의 성능지표로는 MAE와 RMSE를 사용하였고, 이를 통해 본 연구에서 제안하는 GRU 모델의 유용성을 제시하였다. 분석결과 제안한 GRU 모델이 전통적인 기법인 다중선형회귀 모델과 최근 각광받고 있는 LSTM 모델 및 Conv-LSTM 모델보다 예측 정확도가 높게 나타났다. 또한 분석에 소요되는 시간도 GRU 모델이 LSTM 모델, Conv-LSTM 모델보다 짧았다. 본 연구를 통해 서울시 공공자전거의 수요예측을 보다 빠르고 정확하게 하여 향후 재배치 문제 등의 해결에 도움이 될 수 있을 것이다.

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

본 연구는 19대 전반기 국회의원의 속성에 따른 입법정보 이용행태에 대한 분석을 통해 20대 전반기 국회의원의 입법정보 수요예측과 입법정보서비스의 정책적 함의를 찾는 것을 목표로 한다. 입법정보 지원기관인 국회도서관의 참고질의회답을 분석대상으로 하고, 의원의 정치속성과 관계속성을 독립변수로 하여 분석한 결과 소속정당 선출방식 내향중심성에 따라 국회도서관의 입법정보 이용에 유의미한 차이가 있었다. 이와 함께 20대 국회가 여소야대의 국회의원 구성과 3당 체제의 원내구성이라는 점에서 국회도서관의 입법정보 수요가 증가될 것이라고 예측할 수 있었다.

The purpose of this study is to find a policy and to predict the needs of legislative information service of the 20th National Assembly. For this purpose, It is critical to understand the use behavior of legislative information service according to the attribute for the member of the 19th National Assembly. Thus, this study examined the results of reference service of National Assembly Library of Korea using the politics attribute and the relation attribute as independent variables for the member of the National Assembly in the First Half of the 19th National Assembly. Consequently, there were meaningful differences in the use of legislative information service between users by party affiliation, method of an election and introversion. Also, the increased demand of legislative information service was predicted in that the 20th National Assembly is the status of the opposition majority and the three major parties.

Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

Load forecasting is essential to the economical and the stable power system operations. In general, the forecasting days can be classified into weekdays, weekends, special days and special light-load periods in short-term load forecast. Special light-load periods are the consecutive holidays such as Lunar New Years holidays, Korean Thanksgiving holidays and summer special light-load period. For the weekdays and the weekends forecast, the conventional methods based on the statistics are mainly used and show excellent results for the most part. The forecast algorithms for special days yield good results also but its forecast error is relatively high than the results of the weekdays and the weekends forecast methods. For summer special light-load period, none of the previous studies have been performed ever before so if the conventional methods are applied to this period, forecasting errors of the conventional methods are considerably high. Therefore, short-term load forecast for summer special light-load period have mainly relied on the experience of power system operation experts. In this study, the trends of load profiles during summer special light-load period are classified into three patterns and new forecast algorithms for each pattern are suggested. The proposed method was tested with the last ten years' summer special light-load periods. The simulation results show the excellent average forecast error near 2%.

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.

본 연구는 2000년 1사분기부터 2010년 4사분기 까지 인천국제공항에서 출발하여 유럽내 모든공항에 도착한 항공화물의 시계열 자료를 바탕으로 SARIMA 모형을 활용, 수요예측 모형을 구축하였다. 또한 SARIMA 모형을 활용하여 구축한 예측모형을 기존에 주로 활용되어진 ARIMA 모형과 그 예측정확성을 비교 분석함으로써 SARIMA 모형의 정확성을 확인하였다. 현재 국내교통수요를 예측하는 부문에 있어서 SARIMA 모형을 활용한 경우는 극히 드물다. 또한 공항의 총 여객수요나 화물량이 아닌 항공노선의 수요예측에 관한 연구 역시 찾아보기 힘들다. 이러한 상황 하에서, SARIMA 모형을 활용하여 인천국제공항 발 유럽노선의 항공화물 수요를 예측한 본 연구는 상당히 큰 의미가 있다고 생각된다.

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.

For optimal reservoir operation and management, there are essential elements including water supply in agricultural reservoir and demand in irrigation district. To estimate agricultural water demand and supply, many factors such as weather, crops, soil, growing conditions cultivation method and the watershed/irrigation area should be considered, however, there are occurred water supply impossible duration under the influence of the variability and uncertainty of meteorological and hydrological phenomenon. Focusing on agricultural reservoir, amount and tendency of agricultural water supply and demand shows seasonally/regionally different patterns. Through the analysis of deviation and changes in the timing of the two elements, duration in excess of water supply can be identified quantitatively. Here, we introduce an approach to assessment of irrigation vulnerable duration for effective management of agricultural reservoir using time dependent change analysis of residual water supply and irrigation water requirements. Irrigation vulnerable duration has been determined through the comparison of water supply in agricultural reservoir and demand in irrigation district based on the water budget analysis, therefore can be used as an improved and basis data for the effective and intensive water management.

평일의 전력수요는 연속적인 시계열 특성이 뚜렷하여 전력수요예측 오차가 크지 않으나 특수일의 경우는 불연속적인 시계열특성을 가지게 되어 전력수요예측 오차율이 크다. 특히, 연휴의 직전 평일은 평일의 특성과 특수일의 특성이 혼재하고 있어 오차율이 가장 큰 일자 중 하나이다. 따라서 본 논문에서는 연휴 직전 평일과 직전 일요일과의 부하 변동비를 계산하여 전력수요를 예측하는 방법을 제안하고, 추석연휴 직전 평일에 제안한 방법을 적용하여 최대수요예측 오차가 개선됨을 확인하였다.

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.