생명보험산업 보험료 성장률 예측계량모형 비교

전성주 ( Sungju Chun ),조영현 ( Younghyun Cho ) 한국금융연구원 2015 금융연구 Vol.29 No.3

In this article we evaluate the performance of forecasting models to predict the Korean life insurance premium growth rates. Comparisons are made for the Vector Auroregressive (VAR) predictive model, the multivariate leading indicator model, and the diffusion index model proposed by Stock and Watson (2002) against the Univariate Autoregressive (AR) predictive model as a benchmark. We compare each model’s predictability for the total premium incomes and the initial premium incomes of three types in the individual insurance; protection, endowment and annuity. The complete quarterly data spans from the second quarter of 1986 to the first quarter of 2014. The lag selection for AR and VAR forecasting models depends on the Bayesian Information Criteria (BIC) with the maximum number of lags set to 4. For the multivariate leading indicator model, we use 4 leading indicators of GDP growth rates, inflation, education, and age. In order to avoid data mining concerns, we select the variables that have been found to be the determinants of life insurance demands by previous studies. The diffusion index model is an approximate dynamic factor model that relates the future life insurance premium growth rates to a number of factors estimated by principal components using a large number of macroeconomic variables. The set of macroeconomic variables consists of 57 variables representing 6 main categories of macroeconomic time series: demand for final output; balance of payments and international trade; price indexes; money, interest rates and financial markets; labor, production and population; and world variables. We also include in the data set the variables related to the life insurance industry such as the industry’s total asset returns, claims paid and etc. In predicting total premium income growth rates, the AR predictive model produces the smallest mean squared predictive errors (MSPEs). For the premium incomes of protective insurance all the forecasting models have the MSPEs less than 3%. But they become unreliable in predicting the premium incomes of annuity with producing the MSPEs more than 10%. When we test the null hypothesis of no difference in MSPEs, it is rejected at 5% significance level for the VAR and the diffusion index predictive models when we forecast the total premium income growth rates of protective insurance. In predicting initial premium income growth rates, we find that there are no statistically significant differences in the MSPEs of each model. In addition, all the models have MSPEs more than 10% so that we may not be able to depend on any model to forecast in practice. We conclude that it may not be beneficial to take advantage of the information contained in macroeconomic variables for predicting life insurance premium growth rates. It may be due to the fact that most of the insurance contracts in Korea charge monthly premiums, which induces heavy autocorrelations among quarterly insurance premium data and makes an AR forecast very effective. Rho and Shin (1998) also found that macroeconomic variables are not likely to influence on insurance demands as is largely determined by insurer’s push-marketing. Policy holders cannot surrender their insurance contracts without heavy penalties, which makes them not so much responsive to macroeconomic environments. Lastly, life insurance demands are very sensitive to changes in insurance regulation and taxation, which could not be controlled for by our estimation procedure due to the lack of time series observations.

Model Predictive Current Control Strategy for Improved Dynamic Response in Cascaded H-Bridge Multilevel Inverters

Baek Ju-Yoen,Lee Kyo-Beum 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3

This paper proposes a model predictive current control technique for fast dynamic response in cascaded H-bridge multilevel inverters. Typically, cascaded H-bridge multilevel inverters are controlled using the PI controller with a pulse-width modulation or the model predictive control method through system modeling. However, PI controller has a limitation in terms of dynamic responses. In contrast, the model predictive control method does not require tuning of proportional and integral gain values and does not use pulse-width modulation. Thus, the model predictive control has a fast response and a fexible control technique. Applying the conventional model predictive control to multilevel inverters results in a large amount of computation because of a large number of voltage vectors. The proposed method suggests a model predictive control strategy to determine output voltage vectors and switching states based on the position of a reference voltage. Furthermore, the proposed method reduces the computation compared to conventional model predictive control techniques, which calculate all voltage vectors, and achieves faster responses than conventional method that uses adjacent vectors. The efectiveness of the proposed model predictive current control method was validated through simulation and experimental results.

Prediction Model of Sports Performance Based on Grey BP Neural Network

Deng Kui,Xiao Liu,Xu Liang,Song Haiyan 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.8

The best annual performances of the world women’s pentathlons during 2005~2013 are statistically collected in this article, and the prediction of the best performance of the world women’s heptathlon in 2013 is taken as the research object. According to the best annual performances of the world women’s heptathlons during 2005~2012, the sports performance prediction model composed of GM(1,1) grey prediction model and BP neural network prediction model in serial connection is established in this article, and this model is applied to predict the best annual performance of the world women’s heptathlon in 2013. Through the comparison of the actual value of the best annual performance of the world women’s heptathlon in 2013 and the predicted value of the model, the application of the grey BP neural network prediction model in sports performance prediction is researched and analyzed in this article. The research result shows that for the sports performance prediction problem, the grey BP neural network prediction model has the features of high prediction accuracy, simple application and strong generalization performance, and this model is also superior to single GM(1,1) grey prediction model and BP neural network model.

한국 프로야구 관중 수 예측

채진석 ( Jin Seok Chea ) 한국스포츠정책과학원(구 한국스포츠개발원) 2012 체육과학연구 Vol.23 No.4

프로야구는 프로스포츠 종목 중에서 관중 수가 가장 많으며 경제적인 파급효과 또한 큰 인기 종목이다. 프로야구 관중 수의 증가가 생산 활동의 결과라 한다면 정확하고 신뢰성 있는 관중 수 예측은 비즈니스 활동에 중대한 영향을 미칠 것이며 새로운 구단을 창단 할 필요성이 제기 될 때 KBO와 각 기업의 의사결정에 한 축이 될 것이다. 입장수입이 전체수입에 대부분을 차지하는 프로야구에서 관중 없이는 중계권료와 스폰서 광고수입, 부대시설수입, 입장수입 등을 담보 할 수 없다. 따라서 프로야구에서 관중수의 증가 감소는 구단 수입과 직결되므로 관중 수 예측은 그 만큼 중요하며 관중 수 예측에 관한 논문은 단지 과거의 관중현황에 대한 추이분석이나 패턴분석만으론 올바른 미래의 값을 제시하지는 못 할 것이고, 예측변인의 올바른 선택이야말로 올바른 예측모형이 제시 될 것은 자명한 일이다. 이런 연유로 이 연구는 시계열 자료인 프로야구 관중 수를 예측하는 예측모형을 제한하며 이 자료에 적합하다고 예상되는 3가지 예측방법을 분석하였다. 3가지 예측방법 중 첫 번째는 예측 값을 계산하기 위하여 기간에 부여하는 가중치는 과거로 갈수록 지수 함수적으로 감소하며 최근의 자료에 가중치를 부여하는 추세조정 지수평활법(Trend Adjusted Exponential Smoothing)중 Brown 선형 추세모형이 최적의 모형으로 선택 되었고 두 번째는 입력계열이 없는 단변량ARIMA모형(Autoregressive Integrated Moving Average model, 자기회귀누적이동평균모형,) 중 단변량ARIMA (1, 1, 1)모형이 최적의 모형으로 채택 되었으며, 세 번째는 한국프로야구가 창립된 1982년 이후 2011년까지의 연간 관중 수를 종속변인으로 하고, 같은 기간의 GDP, 8개 구단의 홈 도시인구, 경기력, 연간 경기수, 각 팀의 Post시즌 진출 여부를 예측변수로 한 다변량ARIMA(0, 1, 1)모형이 최적의 모형으로 선정 되어, 이모형이 제시한 2012년, 2013년 예측 관중수는 7,825,004명과 8,196,416명으로 예측하였다. The professional baseball game is the most popular sports in korea, which accommodates the largest number of spectators and has a strong impact on the economical contributions to the society and social development. It is assumed that increase in spectator number coming to ball park would be the result of production industry activation and thus the precise and reliable prediction of spectator in the future would impose the serious influence on the wide range of baseball related business industries with additional profits. That would also affect to the decision making process of enterprise and Korean Baseball Organization in establishment of new baseball team. At present the profits from entrance fee to ball park have been held the largest prtions of total profits, and no additional profits from broadcasting sponsors, and subsidiart facilities would be expected without the increases of spectators coming to ball park. The prediction of spectators would be directly related to be the expected profits. Additionally correct prediction of spectator would not be possible only with the trend analysis and/or pattern analysis. Selection of prediction variable would be also critical for setting the prediction models. Therefore the purpose of this study was to investigate 3 prediction models for numbers of spectator expected in future and to propose one of 3 models which dependable and reliable model for predicting the number of spectators and predict the expected number of spectators coming to baseball park in 2012 and 2013. The first model was analyzed to calculate the prediction values, and a Brown linear trend model was selected withint trend adjusted exponential smoothing. As second model ARIMA(1, 1, 1,) was selected as optimum model within autoregressive integrated moving average model. South Korea is the third professional baseball since its founding in 1982 by 2011 the annual number of spectators as dependent variables, and for the same period GDO, 8개 nine urban population of the homev, performance, and annual P, each team entering the season whether or not Post as predictor variable, a multivariate ARIMA (0, 1, 1) model was selected as the optimal model. Selected more than three kinds of model root mean square (RMS) error (RMSE) with the least multivariate ARIMA (0, 1, 1) model is selected as the final model is valid through the 2012 Nell type (7,825,004 people), 2013 (8,196,416 people). Prediction of attendance were presented.

Development of an energy cost prediction model for a VRF heating system

Park, Bo Rang,Choi, Eun Ji,Hong, Jongin,Lee, Je Hyeon,Moon, Jin Woo Elsevier 2018 Applied thermal engineering Vol.140 No.-

<P><B>Abstract</B></P> <P>This study developed a predictive model using artificial neural network (ANN) to forecast the energy cost for a variable refrigerant flow (VRF) heating system. The energy cost is predicted with the ANN model by considering the set-points for the refrigerant condensation temperature, condenser fluid temperature, condenser fluid pressure, and air handling unit supply air temperature together with past operational data and other climatic data. The predicted energy cost was used as a determinant for the control algorithm to optimize the heating system operation in terms of cost.</P> <P>The study consisted of three steps: initial model development, model optimization, and performance evaluation. The neural network toolbox in the Matrix laboratory was used to develop the model and conduct the performance tests. For the model training and performance evaluation, data sets were collected in the winter from a test building.</P> <P>Initial model consisted of a structure that included ten input neurons and a learning method. Then, the optimization process was used to find the optimal structure of the ANN model, which was 1 hidden layer with 15 hidden neurons, while the optimal learning method had a 0.5 learning rate and 0.4 momentum. In the performance evaluation, the optimized model demonstrated its prediction accuracy to be within the recommended level, with 0.8417 r<SUP>2</SUP> and 4.87% coefficient of variation root mean squared error between the measured and the predicted costs, thus proving its applicability in the control algorithm to supply a comfortable indoor thermal environment in a cost-efficient manner.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A predictive and adaptive ANN model was developed for controlling heating system. </LI> <LI> The model predicted heating energy cost for the different variable settings. </LI> <LI> Model optimization was conducted for the accurate and stable prediction. </LI> <LI> The optimized model demonstrated its prediction accuracy within the recommended level. </LI> </UL> </P>

An Agricultural Land Contractual Management Transfer Prediction Model Based on Analytic Hierarchy Process and Logistic Regression

Hai-ying Cao,Ling Wu 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.10

In the field of application prediction, there are two kinds of common methods to establish the prediction model: prediction model established by artificial data analysis relying on expert experience, and prediction model achieved by statistical model exploiting data analysis. However, the prediction accuracy of the model based on expert is restricted by the experiences, while the model based on statistical analysis is limited by the quality and scale of the training data. In view of the advantages and disadvantages of these two kinds of models, this paper presents a prediction model by integrating Analytic Hierarchy Process and Logistic Regression. The proposed prediction model uses the Analytic Hierarchy Process, which are based on the training data and expert experience to obtain the rank of predominant factor in a specific domain, and exploits the logistical regression model to learn the weights of each influencing factor. Finally, the linear combination of the two models is used to obtain the prediction model. Further, we take agricultural land contractual management transfer prediction as an example to test the proposed hybrid prediction model.

환경영향평가에 적용되는 3차원 소음예측모델의 가이드라인 마련

선효성,최준규,박영민 한국환경연구원 2012 수시연구보고서 Vol.2012 No.-

국내 개발사업의 환경영향평가에서 사업지구 주변의 정온시설에 대한 소음영향을 평가하고 있다. 교통시설 증대와 고층 정온시설의 보편화 등으로 인해 소음환경이 복잡해지고 있는 상황에서 실제적인 소음평가를 수행할 수 있는 접근방법으로 3차원 소음예측모델의 활용이 증대되고 있다. 그러나 3차원 소음예측모델의 적용방법에 대한 가이드라인이 전무하여 그에 따른 소음평가결과의 신뢰성 확보에 의문이 제기되고 있다. 따라서 본 연구에서는 3차원 소음예측모델의 내부에 포함된 소음예측식 및 입력변수 등을 검토하여 3차원 소음예측모델을 효율적으로 적용할 수 있는 가이드라인을 모색하였다. 3차원 소음예측모델과 관련한 규정 및 사례 분석을 통해 기존 환경영향평가에서 모델적용에 의한 문제점을 파악하였다. 이를 통해 3차원 소음예측모델에 대한 구체적인 검토를 바탕으로 3차원 소음예측모델을 환경영향평가에서 효율적으로 적용할 수 있는 가이드라인의 필요성을 언급하였다. 환경영향평가에서 3차원 소음예측모델을 효율적으로 적용하기 위한 방안을 모색하기 위해 도로소음을 대상으로 SoundPLAN, Cadna-A, IMMI 상용프로그램을 적용하였다. 이러한 프로그램을 활용하여 도로소음 예측식, 도로소음원 입력자료, 방음벽 및 건물과 관련한 입력자료 등을 근거로 3차원 소음예측모델 구현을 통한 소음예측결과를 비교·검토하였다. 소음예측결과의 비교·검토를 바탕으로 3차원 소음예측모델을 도로소음평가에 적용하기 위한 방안을 제안하였다. The noise impact of the residential facility around a development region is assessed in EIA. As noise environment is complicated due to the increase of a transportation facility and the generalization of a high-rise building, the application of 3-D noise prediction model is increasing as a tool for performing actual noise assessment. However, because the guideline for 3-D noise prediction model does not exist, the reliability of the noise assessment results generated by 3-D noise prediction model is under discussion. Therefore, this study is focused on the development of a guideline for 3-D noise prediction model through the examination of prediction equation and input data in 3-D noise prediction model. In order to find the application plan of 3-D noise prediction model for EIA, The SoundPLAN, Cadna-A, and IMMI commercial programs (3-D noise prediction models) are adopted for road noise. The noise prediction results of three noise prediction models are compared in road noise prediction equation, road noise source input data, barrier and building input data, etc. Based on above comparison results, this paper suggests a guideline for application of 3-D noise prediction model in road noise assessment.

Performance Prediction Model of University Students Based on the Grey BP Neural Network

Liao Yu,Liu Zongxin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.10

This article counted the best performance of students entrepreneurship courses from 2005 to 2014, and took the best performance prediction of 2014 entrepreneurship course as the research object. According to the best annual performance of entrepreneurship courses from 2005 to 2014, this article established the grade prediction model of series combination of GM (1, 1) grey prediction model and BP neural network prediction model, and the established model was used to predict the best annual performance of students entrepreneurship course. Through comparing the actual value of the best annual performance of 2014 entrepreneurship course and the predicted value c by the model, this article analyzed the application of grey BP neural network prediction model in the students entrepreneurship performance prediction. The research results showed that for entrepreneurship performance prediction problem, the grey BP neural network prediction model had high prediction precision , simple application, and it can be widely used, and had more advantages than single GM (1, 1) grey prediction model and BP neural network model.

Short-term Predictive Models for Influenza-like Illness in Korea:Using Weekly ILI Surveillance Data and Web Search Queries

정재운 한국디지털정책학회 2018 디지털융복합연구 Vol.16 No.9

Since Google launched a prediction service for influenza-like illness(ILI), studies on ILI prediction based on web search data have proliferated worldwide. In this regard, this study aims to build short-term predictive models for ILI in Korea using ILI and web search data and measure the performance of the said models. In these proposed ILI predictive models specific to Korea, ILI surveillance data of Korea CDC and Korean web search data of Google and Naver were used along with the ARIMA model. Model 1 used only ILI data. Models 2 and 3 added Google and Naver search data to the data of Model 1, respectively. Model 4 included a common query used in Models 2 and 3 in addition to the data used in Model 1. In the training period, the goodness of fit of all predictive models was higher than 95% (R2). In predictive periods 1 and 2, Model 1 yielded the best predictions (99.98% and 96.94%, respectively). Models 3(a), 4(b), and 4(c) achieved stable predictability higher than 90% in all predictive periods, but their performances were not better than that of Model 1. The proposed models that yielded accurate and stable predictions can be applied to early warning systems for the influenza pandemic in Korea, with supplementary studies on improving their performance.

운용 형태별 원샷 시스템의 신뢰도 예측 모델 제안

서양우(Yang-Woo Seo),엄천섭(Chun-Sup Um),남현우(Hyun-Woo Nam),전동주(Dong-Ju Jeon) 한국산학기술학회 2022 한국산학기술학회논문지 Vol.23 No.3

본 논문에서는 운용 형태별 원샷 시스템의 신뢰도 예측 모델을 제시하였다. 운용개념에 따른 운용 형태별 원샷 시스템의 신뢰도 예측 모델을 제시한 후 신뢰도 예측을 위한 절차를 제안하였다. 제안한 신뢰도 모델에 대한 사례분석을 통하여 모델간의 차이점을 분석하였다. Operational Model, Mixing Model, Storage Model 순으로 운용환경 조건이 가혹하기 때문에 고장률이 더 높음을 확인할 수 있었다. 따라서, 비운용 시간이 대부분인 원샷 시스템의 고장률은 상당히 적게 산출되기 때문에 운용개념에 따라 세분화하여 신뢰도를 산출해야 한다. 이 결과들을 활용하여 신뢰도 80% 이상 목표 값이 주어질 때 각각의 신뢰도 예측 모델별로 점검 시점을 확인하였다. 운용탄에서는 1년, 혼용탄에서는 1.3년, 저장탄에서는 5.6년 시점에서 점검을 수행해야 하는 결과를 도출하였다. 본 연구의 결과로 제시한 운용 형태별 원샷시스템의 신뢰도 예측 모델을 기반으로 정확한 신뢰도 예측이 가능하다고 판단된다. 따라서, 본 연구는 어떠한 원샷 시스템이든 신뢰도 예측시 모델 기반으로 활용 가능하다. 현 시점에서의 모델들을 한국군 데이터 기반의 모델이 아니라는 제한사항이 있기에 향후에는 한국형 고장데이터를 포함한 보완된 모델을 제시할 필요가 있다. This research proposed a reliability prediction model of One-shot systems by operational mode. The procedure for reliability prediction was proposed after presenting the reliability prediction model for each operational mode according to the operational concept. The differences between models were analyzed through case analysis for the proposed reliability prediction. It was confirmed from the analysis that the failure rate was higher because the operating environmental conditions were harsh. Harshness was low for the operational model, followed by the mixing and storage models in the same order. Since the failure rate of the One-shot system, which has most of the non-operating time, is calculated to be quite small, reliability should be calculated by subdividing it according to the operational concept. Subsequently, each reliability prediction model"s inspection timing was checked using the results of the calculations when a target value of 80% or more reliability is given. The analysis results indicate that the inspection had to be performed every one year for the operational missile, 1.3 years for the mixed missile, and 5.6 years for the storage missile. It is also concluded that accurate reliability prediction is possible based on the reliability prediction model of the One-shot system by operational mode presented. Therefore, this study can be used as a model base while predicting the reliability of any One-shot system. Since the models presently used are not Korean military data-based models, it is necessary to present a supplemented model including Korean-style failure data in the future.