• Water for future
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• v.22 no.3
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• pp.331-339
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• 1989

The monthly flows with periodicity and trend were forecasted by multiplicative ARIMA model and then the applicability of the model was tested based on 23 years of the historical monthly flow data at Jindong river stage gauging station in the Nakdong River Basin. The parameter estimation was made with 21 years of data and the remaining two years of monthly data were used to compare the forecasted flows by ARIMA (2,0,0)$\times$$(0,1,1)_{12}$ with the observed. The results of forecast showed a good agreement with the observed, implying the applicability of multiplicative ARIMA model for forecasting monthly river flows at the Jindong site.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.97-105
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• 2006

Brand Pricing is the most important issue for the brand manager in the dynamic market. in the typical dynamic pricing model, a linear function has been used based on the assumption that the non-Price Influences and the price influences were independent. However, to incorporate the characteristics of the dynamic market, it is natural to consider the multiplicative relationship. We are going to try the multiplicative linkage between the non-price Influences and the price influences and suggest a new dynamic pricing model with e multiplicative functional form. An empirical study of 19 brands in the Korean cigarette market shows the feasibility of the suggested model.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.3
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• pp.156-184
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• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• Proceedings of the Korea Water Resources Association Conference
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• 1989.07a
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• pp.31-44
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• 1989

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.35-42
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• 2001

The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.111-117
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• 2008

This paper presents a new continuation-based quasi-steady-state(CQSS) time-domain simulation algorithm incorporating a multiplicative aggregated load model for power systems. The authors' previous paper introduced a CQSS algorithm, which has the robust convergent characteristic near the singularity point due to the application of a continuation method. The previous CQSS algorithm implemented the load restoration in power systems using the exponent-based load recovery model that is derived from the additive dynamic load model. However, the reformulated exponent-based model causes the inappropriate variation of short-term load characteristics when switching actions occur, during time-domain simulation. This paper depicts how to incorporate a multiplicative load restoration model, which does not have the problem of deforming short-term load characteristics, into the time simulation algorithm, and shows an illustrative example with a 39-bus test system.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.825-832
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• 2012

We propose a novel way of forecasting the market shares of several brands simultaneously in a multiplicative competitive interaction model, which uses kernel regression technique incorporated with kernel machine technique applied in support vector machines and other machine learning techniques. Traditionally, the estimations of the market share attraction model are performed via a maximum likelihood estimation procedure under the assumption that the data are drawn from a normal distribution. The proposed method is shown to be a good candidate for forecasting method of the market share attraction model when normal distribution is not assumed. We apply the proposed method to forecast the market shares of 4 Korean car brands simultaneously and represent better performances than maximum likelihood estimation procedure.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.127-136
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• 2011

Multiplicative noise model is the one of popular method for masking continuous variables. In this paper, we propose the transformation on the variable to which random noise was multiplied. An advantage of the masking method using proposed transformation is that the masking data users can obtain the unbiased values of mean and variance of original (unmasked) data. We also consider the data utility and correlation structure of variables when we apply the proposed multiplicative noise scheme. To investigate the properties of the method of masking based on multiplicative noise, a simulation study has been conducted using the 2008 Householder Income and Expenditure Survey data.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.1-11
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• 1995

The parameters in the multiplicative model $Y_{1}={\alpha}_{0}{\prod}^{p}_{k=1}X_{kj}^{{\beta}_K}v_{j}$ are usually estimated by the least squares method after logarithmic transformation, and the least square Estimator of ${\alpha}_{0}$ is known to be biased, i.e., $E(e xp(\hat{\beta}_{0})){\neq}{\alpha}_{0})$. In the present study the unbaised estimators of ${\alpha}_{0}$ are examined(1) by modifying the least squares estimator and (2) by applying the Finney's results. The variances are also compared. In addition it has been observed that multiplicative model can be used to express the relationship beetween rice yield and yield components.