• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.421-426
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• 2024

The Analytic Hierarchy Process (AHP) converts people's judgment criteria into objective numerical values using pairwise comparisons. However, the need for an excessive number of pairwise comparisons poses a problem. To mitigate this issue, most existing studies have utilized the process separation approach. The method of process separation devised in this study is a "separation and integration approach," where 1) the standard AHP process is used for evaluating judgment criteria, and 2) the Multi-Attributive Utility Technique (MAUT) is applied for comparing alternatives. This AHP-MAUT Hybrid model was applied to a real analysis case, specifically analyzing the transportation choices of commuters between Bundang and Gangnam Station in Gyeonggi Province. The results showed that the computational process was reduced by 42.03% when applying the Hybrid model compared to using the AHP model alone. Furthermore, the choice results of residents using the Hybrid model were compared with those using the standard AHP. The consistency between the two models' choices was 82.1%, indicating a significant level of consistency. In conclusion, this study contributes by presenting a simpler, more convenient, yet equally effective Hybrid model as a new decision-support system alternative to AHP.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.525-537
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• 2016

The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.379-387
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• 2002

This paper presents an analytical model on nonlinear hysteretic behavior of hybrid steel beam with reinforced concrete ends. The modeling method and appropriate coefficients with IDARC2D were proposed from the comparison with previous test results. Since the polygonal model of IDARC2D nay overestimate, new analytical model with the initial stiffness reduction coefficient was proposed. The hysteretic coefficients for the analysis of the hybrid steel beam with reinforced concrete ends were also presented. The analytical results were compared with previous experiments. The initial stiffness and the strength were predicted with less than 5% error and 10% error, respectively.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.677-686
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• 2018

There remain numerous ungauged watersheds in Korea owing to limited spatial and temporal streamflow data with which to estimate hydrological model parameters. To deal with this problem, various regionalization approaches have been proposed over the last several decades. However, the results of the regionalization models differ according to climatic conditions and regional physical characteristics, and the results of the regionalization models in previous studies are generally inconclusive. Thus, to improve the performance of the regionalization methods, this study attaches hydrological model parameters obtained using a spatial proximity model to the explanatory variables of a regional regression model and defines it as a hybrid regionalization model (hybrid model). The performance results of the hybrid model are compared with those of existing methods for 37 test watersheds in South Korea. The GR4J model parameters in the gauged watersheds are estimated using a shuffled complex evolution algorithm. The variation inflation factor is used to consider the multicollinearity of watershed characteristics, and then stepwise regression is performed to select the optimum explanatory variables for the regression model. Analysis of the results reveals that the highest modeling accuracy is achieved using the hybrid model on RMSE overall the test watersheds. Consequently, it can be concluded that the hybrid model can be used as an alternative approach for modeling ungauged watersheds.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.145-154
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• 2009

The main goal of this paper is to develop a methodology for increasing the reliability of truck OD matrices in Seoul Metropolitan Area. We propose a Hybrid Method made up of five processes by using Non-Traffic Assignment and Gradient Method. A Hybrid Method and Gradient Method have applied for comparison and estimation in Seoul Metropolitan Area. Mean Error and Root Mean Square Error of a Hybrid Method present lower than Gradient Method. The findings of this paper show that the new truck OD matrices created by a Hybrid Method are more reliable than the existing truck OD matrices in the Seoul Metropolitan Area.

• Journal of Radiation Protection and Research
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• v.29 no.4
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• pp.269-273
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• 2004

The hybrid dead time model adopting paralyzable (or extendable) and non-paralyzable (or non-extendable) dead times has been introduced to extend the usable range of G-M counters in high counting rate environment and the relationship between true and observed counting rates is more accurately expressed in the hybrid model. GMSIM, dead time effects simulator, has been developed to analyze the counting statistics of G-M counters using Monte Carlo simulations. GMSIM accurately described the counting statistics of the paralyzable and non-paralyzable models. For G-M counters that follow the hybrid model, the counting statistics behaved in between two idealized models. In the future, GMSIM may be used in predicting counting statistics of three G-M dead time models, which are paralyzable, non-paralyzable and hybrid models.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.254-262
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• 2005

본 연구에서는 중학교 과학 영재아들이 가지고 있는 조석에 관한 정신모형을 탐색하기 위하여 상황에 따른 개념 유형을 분류하고, 각 유형들을 구성하는 국면들을 분석하였다. 조석 현상에 관해 두 가지 상황으로 구성된 과제 수행을 실시한 후 그 응답 결과를 분석한 결과, 상당수의 학생들이 상황에 따라 서로 다른 개념 유형을 나타내었다. 상황에 따른 개념 유형들을 구성하고 있는 국면을 분석한 결과, 각 유형들은 내용-일반적 국면을 공통적으로 포함하고 있었으나, 내용-특정적 국면과 전략적 국면에서는 많은 차이를 나타내었다. 두 가지 상황에서 나타나는 개념 유형들과 이들 유형을 구성하는 국면들을 조합하여 학생들의 정신모형을 분석한 결과 다음과 같은 4가지 모형으로 나눌 수 있었다: (1) Tide model (2) Force model (3) Phase model (4) Hybrid model. Tide model은 과학적으로 옳은 모형이며, Force model과 Phase model은 옳지 않은 모형이며, Hybrid model은 혼합 모형으로 상황에 따라 나타나는 개념 유형이 서로 부합되지 않는 모형이다. 중학교 과학 영재아들이 조석 현상에 대해 가장 많이 가지고 있는 모형은 Tide model(45.0%)이었으며, 그 다음으로는 Hybrid model(30.0%), Force model(12.5%), Phase model(7.5%) 순으로 나타났다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.9 no.3
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• pp.125-131
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• 1997

In order to analyze the feasibility of the drain layer construction method, which is one of the beach protection methods, a hybrid model is constructed by combining the wave model and the seepage flow model. The used wave model is the analytic solution given by Shuto (1972). and the seepage flow model is used by Richards equation which governs the saturated-unsaturated flow in the porous media. It is concluded by the sensitivity analysis of the hybrid model that the most sensitive parameter in the flow field within the beach is the saturated hydraulic conductivity. The developed hybrid model will be efficiently used in the analysis of the parameter when the drain layers are constructed in the beach, if the field datas are obtained more.