An Empirical Study on the Critical Success Factors in Implementing Appropriate Demand Forecasting Model for Optimum Feeding Number of persons in Large Feeding Organization

김정범 한국유통경영학회 2014 유통경영학회지 Vol.17 No.5

The purpose of this paper is to do the case study of the optimized forecasting model for feedingnumber of persons in large feeding facilities. Much of the forecasting literature concentrates onforecasting tools in R&D environment. This study envisions the forecasters with intensive interestin real cases of demand forecasting model use, and often with highly significant business resultsfrom the perspective view points. The focal point of this case study is to find out the critical success factors in implementation ofoptimum forecasting tools at large feeding business company using demand forecasting model withsurvey results from business users. Through conducting hypothesis verification, the result of thisempirical study is as following. First, Technical Critical Success Factors(Selection of System addressing requirements, Experienced SI project team, Education training & Technology Transfer)have influence on User Satisfaction and Acceptability of Forecasting Model. Second, OrganizationalCritical Success Factors(Top management Sponsorship, Strategic Decision, Change Managementteam) have influence on User Satisfaction. Third, Environmental Critical Success Factors(Level ofdeployment, System Infra: H/W, Network, S/W) have influence on the Acceptability of ForecastingModel. Forth, User Satisfaction(Level of Satisfaction, Benefit, Value) have influence on theAcceptability of Forecasting Model(Response time, Usability, Suitability). Fifth, User Satisfactionand Acceptability of Forecasting Model have influence on the ROI(Reduction of time for new menudevelopment, Reduction of time for menu change, Profit Increase, Reduction of time for data search,Reduction of time for data input, Data Accuracy, Reduction of Scrap, Error Reduction).

장기기억 속성을 이용한 주가 변동성 예측에 관한 연구

박재곤 ( Jae Gon Park ),이필상 ( Phil Sang Lee ) 한국금융학회 2009 금융연구 Vol.23 No.4

본 논문에서는 장기기억 속성을 이용하여 우리나라 주가 변동성을 예측하고 예측성과를 비교한다. 이를 위해 GARCH 모형과 EGARCH 모형에 분수적분 과정을 도입한 FIGARCH 모형과 FIEGARCH 모형을 이용하여 표본 외 기간을 예측하고, 이들 모형의 예측성과가 단기기억 변동성 모형(GARCH 모형)의 예측성과에 비해 우월한지를 비교한다. 분석 결과 우리나라 주가 변동성에 대해 다음과 같은 사실을 발견하였다. 첫째, 주가에서는 장기기억 속성이 나타나지 않은 것과는 달리 주가 변동성에서는 장기기억 속성이 뚜렷하게 나타났다. 둘째, FIGARCH(1, d, 0) 모형과 FIEGARCH(1, d, 0) 모형의 예측성과가 GARCH(1, 1) 모형의 예측성과에 비해 우월한 것으로 나타났다. 그리고 장기기억 변동성 모형의 상대적 예측성과는 예측기간이 길 때 더 우월한 것으로 나타났다. 본 연구의 결과 장기기억 속성을 이용한 변동성 모형은 예측의 정확도를 높일 수 있는 것으로 나타나, 파생상품의 가격결정이나 VaR 측정 등 위험관리에 유용하게 사용될 수 있을 것으로 기대된다. This paper investigates the long-memory property in estimating and forecasting Korean stock market return volatility. Volatility is a central role in derivative pricing, portfolio allocation, risk management, and performance evaluation of funds. In consequence, there has been much research on estimating and forecasting return volatility. Little has been studied about forecasting return volatility, however, by exploiting the long-persistent property in Korean stock market. In this paper, we estimate and forecast return volatility by employing the long-memory property. For this purpose, we use the Fractionally Integrated GARCH and Fractionally Integrated EGARCH models. The estimation results and forecasting performance of the long-memory volatility models are compared with those obtained from the short-memory volatility model such as GARCH model. Many studies suggest that the conditional volatility of stock returns follows a long-memory process; shock dissipates at a slow hyperbolic rate. This type of persistence cannot be appropriately modeled by standard GARCH type models. In this aspect, the long-memory volatility models are needed to explain the high-persistent volatility. Baillie et al. (1996) and Bollerslev and Mikkelsen (1996) suggest the FIGARCH and FIEGARCH models which introduced the high-persistent property in the standard GARCH and EGARCH models. Therefore, we used these long-memory volatility models in forecasting Korean stock market return volatility. We identified some key findings from the results. First, the auto- correlations for the absolute and squared returns decline at very slow rate which suggest that there is a long-memory property in Korean stock return volatility. Second, it is difficult to say that there is a high-persistent property in the level of stock returns. However, the return volatility follows a long-memory process. The estimated values of long-memory parameters, d of FIGARCH (1, d, 0) and d of FIEGARCH (1, d, 0) models, are 0.356 and 0.584, respectively, and are statistically significant at the 1% significance level. Third, we conducted out-of-sample one-step-ahead and ten-step- ahead forecasts using the FIGARCH (1, d, 0) and FIEGARCH (1, d, 0) models and compared the volatility forecasts of both fractionally integrated volatility models with those of the GARCH (1, 1) model as a benchmark. We found that the long-memory volatility models produce superior out-of-sample forecasts in terms of root mean squared error (RMSE), mean absolute error (MAE), and R2 of Mincer-Zarnowitz regression. In addition, relative forecasting performance of the ten day ahead forecasts is better than that of the one day ahead forecasts. These findings suggest that the long-range volatility models are useful tools in forecasting the volatility of asset returns as well as pricing derivatives and hedging risks. The results of this study also will facilitate to induce variance swaps and variance futures in the Korean financial market.

대형급식조직에서 최적급식인원을 위한 적절한 수요예측모델을도입하는데 있어서 주요성공요인들에 관한 실증적 연구

( Jeong Beom Kim ) 한국유통경영학회(구 한국유통정보학회) 2014 유통정보학회지 Vol.17 No.5

본 연구는 대형급식을 하는 조직에서 최적급식인원을 위한 적절한 수요예측모델을 도입하는데 있어서 주요성공요인들에 대한 실증적인 연구를 하는데 목적이 있다. 대부분의 예측에 대한 연구문헌은 연구 및 개발(R&D) 환경상황에서 예측도구들 에 집중하였다. 본 연구는 수요예측 담당자들에게수요예측모델 사용의 실제적 사례에서 함축적인 흥미와 조직 전체적인 견지에서 종종 매우 중요한사업결과에 대한 비전을 제시하고 있다. 본 연구의 초점은 산업현장 수요자들에 부터의 설문지 결과를 통해, 대형급식을 하는 사업장에서최적의 수요예측도구를 도입하는데 있어서 주요성공요인들을 찾아내는데 있다. 가설검증을 통하여,본 연구의 실증적인 결과는 다음과 같다. 첫째, 기술적 주요성공요인(요구사항에 맞는 시스템선정, 숙련된 SI 프로젝트팀, 교육훈련 및 기슬이전)은 사용자 만족 과 수요예측모델 수용에 영향을 준다. 둘째, 조직적 주요성공요인(최고경영층 지원, 전략적 결정, 변화관리팀)은 사용자 만족에 영향을 준다. 셋째, 환경적인 주요성공요인(실행수준, 시스템 인프라: 하드웨어, 통신네트워크, 소프트웨어)은 수요예측모델 수용에 영향을 준다. 넷째, 사용자 만족(만족수준, 이익, 가치)은 수요예측모델 수용(응답시간, 사용도, 적절성)에 영향을 준다. 다섯째, 사용자 만족과 수요예측모델 수용은 ROI(새로운 음식메뉴 개발시간 단축, 메뉴변경 시간감축, 이익증가, 자료조사 시간감축, 자료입력 시간감축, 자료 정확도, 음식물쓰레기 감소, 오류 감소)에 영향을 준다. The purpose of this paper is to do the case study of the optimized forecasting model for feedingnumber of persons in large feeding facilities. Much of the forecasting literature concentrates onforecasting tools in R&D environment. This study envisions the forecasters with intensive interestin real cases of demand forecasting model use, and often with highly significant business resultsfrom the perspective view points. The focal point of this case study is to find out the critical success factors in implementation ofoptimum forecasting tools at large feeding business company using demand forecasting model withsurvey results from business users. Through conducting hypothesis verification, the result of thisempirical study is as following. First, Technical Critical Success Factors(Selection of System addressing requirements, Experienced SI project team, Education training & Technology Transfer)have influence on User Satisfaction and Acceptability of Forecasting Model. Second, OrganizationalCritical Success Factors(Top management Sponsorship, Strategic Decision, Change Managementteam) have influence on User Satisfaction. Third, Environmental Critical Success Factors(Level ofdeployment, System Infra: H/W, Network, S/W) have influence on the Acceptability of ForecastingModel. Forth, User Satisfaction(Level of Satisfaction, Benefit, Value) have influence on theAcceptability of Forecasting Model(Response time, Usability, Suitability). Fifth, User Satisfactionand Acceptability of Forecasting Model have influence on the ROI(Reduction of time for new menudevelopment, Reduction of time for menu change, Profit Increase, Reduction of time for data search,Reduction of time for data input, Data Accuracy, Reduction of Scrap, Error Reduction).

Forecasting Logistics Demand Using Unbiased GM (1,1) Model Optimized by AIWPSO Algorithm

Li-Yan Geng,Zhan-Fu Zhang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.10

Accurate forecast of logistics demand can provide scientific guidance for logistics planning and decision making. With the complexity and uncertainty characteristics in logistics demand, the forecasting of logistics demand shows comprehensive and complex. The forecasting precision of the traditional forecasting methods often are not satisfying. It is necessary to look for novel forecasting methods to enhance the forecasting precision of logistics demand. Integrating the unbiased GM (1,1) model (UGM (1,1)) into the adaptive inertia weight particle swarm optimization (AIWPSO) algorithm, this paper developed a novel model for forecasting logistics demand, called AIWPSO-UGM (1,1) model, in which the UGM (1,1) model was used to forecast logistics demand and the AIWPSO algorithm was adopted to optimize the grey parameters needed in UGM (1,1) model. Two examples were selected to prove the out-of-sample performance of the AIWPSO-UGM (1,1) model in forecasting logistics demand. The results imply that the proposed AIWPSO-UGM (1,1) model performs better in logistics demand forecasting compared to the GM (1,1) model optimized by AIWPSO algorithm (AIWPSO-GM (1,1)), UGM (1,1), and GM (1,1) models.

단일변량 모형을 이용한 토지가격 예측력 비교

최차순 한국부동산경영학회 2022 부동산경영 Vol. No.

The Purpose of this paper is to compare different models’ forecasting performance of ARIMA, ARIMA-GARCH, ARIMA-RS models in Korean land market covering the period from the first quarter of 1987 to the first quarter of 2022. I carry out in-sample and out-of-sample forecasting power to Korean land price index. For the comparison of forecast powers, I calculate root mean squared forecast errors and mean absoute forecast errors for each forecasting horizon of the second quarter of 2021 to the first quarter of 2022. I test the statistical signification of the predictive comparison results using DM test. The empirical results are as follows: First, the forecasting power of the ARIMA(1,1,0)-GARCH model is the best in all horzions in- and out-of-sample. Second, within the sample, the forecasting power of the ARIMA(1,0) model is relatively better than that of the ARIMA(2,1,0) model, but outside the sample, it is the opposite. In addition, within the sample, the forecasting power of the ARIMA(3,1,0)-RS model is relatively better than that of the ARIMA(1,0)-RS model, but it is contrary to the sample. Third, it is found that the predictive power of the regime-switching model(RSM) is significantly decreased in all predictive horzions in- and out-of-sample. In order to stabilize the real estate market, it is necessary to consider the ARIMA(1,0)-GARCH model as an alternative to the prediction model. 본 연구에서는 ARIMA, ARIMA-GARCH, ARIMA-RS 모형들의 토지가격 예측력을 비교하였다. 분석에 사용된 자료는 1987년 1분기부터 2022년 1분기까지 토지가격지수이며, 예측시계별로 표본내(in-sample)와 표본외(out-of-sample) 예측력을 추정하였다. 예측력은 평균제곱오차제곱근(RMSFE)와 평균절대오차(MAFE)를 적용하였고 각 모형들의 예측력이 통계적 유의성이 있는지 여부에 대해 DM 검정 수행하였다. 분석 결과는 다음과 같다. 첫째, 표본내외의 모든 시계에서 ARIMA(1,1,0)-GARCH 모형의 예측력 가장 우수하게 나타났다. 둘째, 표본내에서는 ARIMA(1,1,0) 모형의 예측력이 ARIMA(2,1,0) 모형보다 상대적으로 더 우수하게 나타났지만, 표본외에서는 반대로 나타났다. 또한 표본내에서는 ARIMA(3,1,0)-RS 모형의 예측력이 ARIMA(1,1,0)-RS 모형의 예측력보다 상대적으로 더 우수하게 나타났지만, 표본외에는 반대로 나타났다. 셋째, 표본내외의 모든 예측시계에서 국면전환모형(RSM)예측력이 현저히 떨어지는 것으로 나타났다. 부동산시장의 안정화를 위해 이분산성의 특징을 잘 표착할 수 있는 ARIMA(1,1,0)-GARCH 모형을 예측 모형의 대안으로 고려할 필요성이 있다.

최적 시계열 수요예측 모델선정에 관한 연구

이충기,송학준 한국관광학회 2007 관광학연구 Vol.31 No.6

This study aims to develop four time-series models in order to select the best model among the time-series models based on MAPE(mean absolute percentage error). The time-series models include various ones including ARIMA model. The first three models have been most popularly used for forecasting tourism demand, whereas the last model of ARIMA Intervention is reported to be more logical and accurate than any other time-series models since special events such as terrorism and mega-events can be incorporated into the model. The results of model estimation indicate that all the four forecasting models were found most accurate in terms of MAPE(Lewis, 1982). Of them the ARIMA Intervention model(MAPE=4.48) appeared to perform best in terms of forecasting accuracy, followed by ARIMA(MAPE=4.96), Winters(5.67), and Stepwise Autoregressive(8.55).핵심용어(Key words):예측정확도(Forecasting accuracy), 윈터스지수평활모델(Winters Exponential Smoothing model), 단계적 자기회귀모델(Stepwise Autoregressive model), ARIMA모델(ARIMA model), ARIMA 개입모델(ARIMA Intervention model). 방한 일본인 관광객은 외래관광객 중 가장 큰 비중을 차지하면서 총 방한 외래관광객의 증감에 주도적 영향을 미치고 있다. 또한, 최근 경제성장과 국가간 교류증가에 따라 중국인 관광객이 미국인 관광객을 제치고 제2의 인바운드 시장으로 부상하고 있는데, 이러한 추세는 <그림 1>을 통해서도 가시적으로 확인할 수 있다.<표 1> 방한 외래객 방문현황과 관광환경 변화연도외래관광객성장률관광환경 변화19902,958,8398.5%-19913,196,3408.0%걸프전19923,231,0811.1%-19933,331,2263.1%대전엑스포 개최19943,580,0247.5%한국방문의 해19953,753,1974.8%-19963,683,779-1.8%-19973,908,1406.1%아시아 금융위기와 IMF19984,250,2168.8%한국경제위기 및 원화약세 지속 19994,659,7859.6%-20005,321,79214.2%인천국제공항개항20015,147,204-3.3%9.11 테러 발생 및 한국방문의 해20025,347,4683.9%한일 월드컵 공동개최20034,753,604-11.1%SARS 발병, 이라크전쟁20045,818,13822.4%한류열풍20056,021,7643.5%한일공동방문의 해 20066,155,0462.2%

DM 검정을 이용한 냉동 고등어 소매가격 예측력 비교 분석

김태현 ( Kim Tae-hyun ),남종오 ( Nam Jong-oh ) 한국도서학회 2016 韓國島嶼硏究 Vol.28 No.3

본 연구의 목적은 통계학적 기법을 활용하여 냉동 고등어의 소매가격을 예측하고, 예측력이 좋은 모형들 중 Diebold & Mariano(DM) 검정을 통해 우수 모형을 선정하는 것이다. 본 연구의 분석 자료는 2006년부터 2015년까지 소매시장의 냉동 고등어 일일가격 자료를 이용한다. 표본내 자료로부터 도출된 예측력이 좋은 모형으로, 우선 개별 정보요인 기준에 따라 ARMA(4,3) 모형과 ARMA(2,1) 모형이 선정한다. 다음으로 동분산의 가정을 충족시키지 못한 ARMA(2,1) 모형에 대해서는 GARCH(1,1) 모형을 선정한다. 끝으로 다른 변수들의 추가적인 정보를 고려한 t-2시차의 VAR 모형을 선정한다. 이상의 선정된 세 가지 모형을 가지고 2015년 1월 2일부터 12월 31일까지의 표본 외 자료를 이용하여 실제치와 예측치를 MSE와 MAE 기준에 근거하여 비교한다. 분석 결과, 표본 내 자료를 이용한 모형 중에서는 ARMA(2,1)-GARCH(1,1) 모형의 예측력이 가장 우수하였다. 이에 동 모형을 기준 모형으로 선정하여 다른 모형 간의 예측력을 비교해 본 결과 자신의 과거 정보와 가격의 변동성을 고려한 ARMA(2,1)-GARCH(1,1) 모형이 ARMA(4,3) 모형과 VAR 모형에 비해 예측력이 가장 우수한 것으로 나타났다. 이상의 결과를 이용하여 냉동 고등어 소매가격을 예측할 수 있다면, 도매상 및 유통업자들에게는 고등어 판매시기 결정에, 소비자들에는 그들의 효용을 극대화시킬 수 있는 구매시기 결정에 유용한 정보를 제공해 줄 수 있을 것으로 판단된다. 아울러, 본 연구의 결과는 정부가 시행하는 수매비축사업과 같은 수산물 시장가격 안정화 정책에도 기초정보 제공적 측면에서 기여할 것으로 보여 진다. Forecasting plays a key role in fishing management policies. The purpose of this study is to select which model is superior to forecast frozen mackerel retail prices using statistical approaches, It also aims to provide policy implications for the frozen retail mackerel price determination model. It especially focuses on the superior forecasting power within the seafood market. This study uses daily frozen mackerel prices of the retail market from 2006 to 2015. ARMA(4,3), ARMA(2,1)-GARCH(1,1), and VAR models with t-2 lag are selected to forecast frozen retail mackerel prices. In addition, the forecasting accuracies of each model are tested using Diebold & Mariano test (1995). It compares the predicted prices and actual prices using 1 year out-of-sample data. The results of this study are as follows. First, the ARMA(2,1)-GARCH(1,1) model shows the most superior accuracy based on MSE and MAE standard. Secondly, in terms of forecasting accuracy, the VAR model is equal to the superior ARMA(2,1)-GARCH(1,1) model only when using MSE standard based on the DM test. The ARMA(2.1)-GARCH(1,1) is the superior model since there is no model that shows identical forecasting accuracy to the MAE standard based on DM test. The outputs of this study are expected to increase consumers` economic utilization, and operate a predictable business to retailers by providing the frozen mackerel retail price future index. Furthermore, the outputs will provide useful information regarding Korean purchase, and also help set projects necessary to stabilize Korea seafood market prices.

머신러닝과 시계열 기법 기반의 초단기 시간단위 수요예측방법론 개발 연구

민경창,하헌구 한국로지스틱스학회 2022 로지스틱스연구 Vol.30 No.3

Demand forecasting is an important field and it is safe to say that forecasting is a key component of economic activity. An accurate forecasting is the key to determining the competitiveness of all economic players. Forecasting an uncertain future is a difficult task and radical change in the external environment are adding to the difficulty of forecasting. Amid the increasing demand for accurate demand forecasting, the emergence of Big data, AI, ML, and DL following the development of computing power is becoming a major turning point in the demand forecasting field as well. In addition to the traditional forecasting methodologies, the use of dataming techniques is also rapidly increasing. And various efforts have been continued to improve the forecasting accuracy. In this paper, a hybrid forecasting methodology which is combined time series model and data mining technique and a multistage methodology are presented for short-term forecasting. Specifically, we developed a hybrid forecasting model that combines SARIMA(Seasonal Autoregressive Integrated Moving Average) and Random Forest, and a multistage methodology that utilizing the forecasting result of the upper-category as a variable in the forecasting process of the sub-category. In order to verify the methodologies presented in this paper, we use the rental data of ‘Seoul bike’(shared bicycle in Seoul) as verification data. As a result of the forecasting ‘Seoul bike’ demand for the next 7 days(every 3 hours) of rental point clusters, the average forecasting accuracy was 81.5%. It is high accuracy level considering that the forecasting unit was 3hours, forecasting horizon was next 56 steps, and the average accuracy by Random forest was 65%. In addition, it was confirmed that high accuracy was maintained steadily regardless of the time difference from the forecasting point unlike the characteristics of general demand forecasting, And the high accuracy level was confirmed as a forecasting model not only a 3 hours forecasting, but also daily(90.1%) and weekly(91.7%) forecasting. The research shows the forecasting methodologies of this paper is worth to use as a short-term forecasting model. And we confirmed that the methodologies are very useful to forecasting daily and weekly demand as well. It is expected that the methodologies proposed in this paper will be widely used as an accurate forecasting model in more diverse fields.

Real-time forecasting of wave heights using EOF – wavelet – neural network hybrid model

Oh, Jihee,Suh, Kyung-Duck Elsevier 2018 Ocean engineering Vol.150 No.-

<P><B>Abstract</B></P> <P>Recently, along with the development of data-driven models, artificial neural networks (ANN) have been used in ocean wave forecasting models. Hybridization of ANN with wavelet analysis or fuzzy logic approach has also been used. The wavelet and neural network hybrid models (WNN models) show better performance than ANN models. However, their accuracy decreases with increasing lead time because they do not consider the relation between wave and meteorological variables. Moreover, the WNN model has been developed to forecast the wave height at a single location where the past wave height data are available. To resolve these problems, in this paper, a hybrid model is developed by combining the empirical orthogonal function analysis and wavelet analysis with the neural network (abbreviated as EOFWNN model). The past wave height data at multiple locations and the past and future meteorological data in the surrounding area including the wave stations are used as input data. The model then forecasts the wave heights at the locations for various lead times. The developed model is employed to forecast the wave heights at eight wave observation stations in the coastal waters around the East/Japan Sea. The EOFWNN model is shown to perform better compared with the WNN model for all lead times regardless of the decomposition level of wavelet analysis. The EOFWNN model is proven to be a promising tool for forecasting wave heights at multiple locations where the past wave height data and the past and future meteorological data in the surrounding area are available.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A real time wave height forecasting model at multiple locations is developed. </LI> <LI> A hybrid model is developed by combining EOF and wavelet analyses with neural network. </LI> <LI> The model forecasts significant wave heights at multiple locations for various lead times. </LI> <LI> The model is applied to eight wave stations in the coastal waters around the East/Japan Sea. </LI> <LI> The model is compared with a single point wave forecast model. </LI> </UL> </P>

평균수명을 이용한 사망률 예측모형 비교연구

정승환,김기환 한국통계학회 2011 응용통계연구 Vol.24 No.1

By use of a mortality forecasting model and a life table, forecasting the average life expectancy is an effective way to evaluate the future mortality level. There are differences between the actual values of average life expectancy at present and the forecasted values of average life expectancy in population projection 2006 from Statistics Korea. The reason is that the average life expectancy forecasts did not reflect the increasing speed of the actual ones. The main causes of the problem may be errors from judgment for projection, from choice, or use of a mortality forecasting model. In this paper, we focus on the choice of the mortality forecasting model to inspect this problem. Statistics Korea should take a mortality forecasting model with considerable investigation to proceed population projection 2011 without the errors observed in population projection 2006. We compare the five mortality forecasting models that are the LC(Lee and Carter) model used widely and its variants, and the HP8(Heligman and Pollard 8 parameter) model for handling death probability. We make average life expectancy forecasts by sex using modeling results from 2010 to 2030 and compare with that of the population projection 2006 during the same period. The average life expectancy from all five models are forecasted higher than that of the population projection 2006. Therefore, we show that the new average life expectancy forecasts are relatively suitable to the future mortality level. 사망률 예측모형과 생명표 작성방법에 기반을 둔 예측평균수명 작성은 미래의 사망수준을 평가하는 효과적인 방법이 된다. 2006년 통계청에서 장래인구추계 작성 시 예측평균수명을 작성하였으나, 2006년 이후 현재까지 실제평균수명과 적지 않은 차이를 보이고 있어 평균수명의 증가속도를 반영하지 못하고 있다. 이의 원인으로는 전망치에 대한 판단, 사망률 예측모형의 선택과 사용 등이 이유가 될 수 있다. 본 논문에서는 사망률 예측모형의 선택관점에서 이 문제를 살펴보고자 한다. 2011년 장래인구추계 작성을 앞둔 상황에서 오류의 반복을 피하기 위해서는 사망률 예측모형에 대한 특성 및 적용가능성에 대한 충분한 검토가 이루어진 후 적절한 모형을 선택해야 할 것이다. 사망률 예측모형은 주로 사용되고 있는 LC(Lee와 Carter) 모형과 이의 개선모형들, 사망확률 확장모형인 HP8(Heligman과 Pollard 8 parameters) 모형 등 모두 5개의 모형을 비교 · 분석하였다. 분석결과를 바탕으로 5개의 모형별로 2030년까지의 남녀별 예측평균수명을 작성하여 제시하였고, 이를 통계청에서 제공하는 예측평균수명과 비교하였다. 5개의 모형에 의해 작성된 2030년까지의 새로운 예측평균수명은 통계청의 결과보다 높게 나타나 실제평균수명의 변화를 상대적으로 잘 반영하는 것으로 나타났다.