• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1657-1667
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• 2007

This study propose a model for railway safety evaluation with which the safety of whole railway system can be evaluated. The evaluation model is to generate a safety index which quantitatively represent the degree of railway safety. Safety index is proposed a function of three indexes; an accident index, safety management index, and safety culture index. This paper describes the first result from the study on the safety target which will be a key starting point toward the development of safety evaluation model. It is recommended that the safety target be composed of several sub-targets that are apportioned to constituent components. It is concluded that the classification of safety target influence on deciding components or attributes that constitute each sub-indexes; an accident index, safety management index, and safety culture index. Based on this study, a railway safety evaluation model will be developed in the next study.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.237-249
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• 2013

Many institutes such as Bank of Korea, Samsung Economic Research Institute and Chamber of Commerce announced Entrepreneurship Index models. But, due to the arbitrary measurement model and the survey of each institute, there is a misled in the understanding and model of entrepreneurship. In addition, there is no research about comparative study of Entrepreneurship Index until now. In this study, we suggest our own Entrepreneurship Index model based on the literature review of Entrepreneurship Index. This model enables the objective measurement of Entrepreneurship Index. Above all, we suggest advantages and disadvantages of existing Entrepreneurship Index model systematically and the direction of Entrepreneurship Index.

• Informatization Policy
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• v.24 no.1
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• pp.48-78
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• 2017

In this study, to reveal determinant factors for degree of open data it conduct empirical analysis for ODB(Open Data Barometer) and OUR Index(Open, Useful, Reusable Government Data Index) that are global open data index among 26 countries. As a result of multiple regression analysis, First focus is on ODB. In the Model 1 with independent variables, e-Government, SW Market size and government efficacy are significantly positive effect for ODB. In the Model 2 with independent variables and moderating variable, e-Government, SW market size and social capital are significantly positive effect for ODB. In the Model 3 with independent variables, moderating variable and interaction term, e-Government and social capital are significantly positive effect for ODB. Second focus is on OUR Index. In the Model 1 and the Model 2 e-Government is significantly positive effect for OUR Index. In the Model, e-Government and SW market size ${\times}$ social capital(interaction term) are significantly positive effect for OUR Index. And in path analysis, only ODB alternative model show Government efficacy with social capital has full mediation effect. In OUR Index alternative model there is no mediation effect with social capital.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.108-114
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• 2012

Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.5
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• pp.632-638
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• 2005

Membrane fouling index such as Silt Density Index (SDI) and Modified Fouling Index (MFI) is an important parameter in design of the integrated RO/NF membrane processes for drinking water treatment. In this study, the effect of particle, membrane and feed water characteristics on membrane fouling index were investigated systematically. Higher fouling index values were observed when filtering suspensions with smaller particle size and higher feed particle concentration. Larger membrane resistance due to smaller pore size resulted in an increased membrane fouling index. The variations of feed water hardness and TDS concentrations did not show any impact on fouling index, suggesting that there were no significant colloidal interactions among particles and thus the porosity of particle cake layer accumulated on the membrane surface could be assumed to be 0.36 according to random packing density. Based on the experimental observations, fundamental membrane fouling index model was developed using Happel Cell. The effect of primary model parameters including particle size ($a_p$), particle concentration ($C_o$), membrane resistance ($R_m$), were accurately assessed without any fitting parameters, and the prediction of membrane fouling index such as MFI exhibited very good agreement with the experimental results.

• Journal of The Korean Association For Science Education
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• v.15 no.1
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• pp.1-5
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• 1995

One of the problem of science education in terms of its status as a unique discipline is the tendency of qualitative, rather than quantitative, arguments and judgements on research activities. In this study, a model called "Diamond Model" and an index formula for the balance of researches are suggested for achieving more pictoricaI and quantitative understandings on the distribution of researches in science education. Diamond Model is consisted of two dimensions corresponding to two main long-debated issues in science education, i.e. the dimension of cognitive-affective and the dimension of concept-process. In Diamond Model the geometrical symmetry represents the the balance of researches. An index formula for the balance was developed in order to ensure that the value of the index is between 0 to 1 and the numerical values of the index corresponds to the geometrical symmetry of the diamond. Then, in order to check their utility, the model and the index formula were applied to analyze the research papers appeared in JKARSE for the last 10 years.