• Journal of the Korea Institute of Building Construction
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• v.14 no.6
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• pp.631-637
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• 2014

Recently, following the application of modular housing techniques to not only residential sector, but also to business sector, the scope of modular housing market b expanding. In the case of other developed countries, such markets are entering into the maturity stage, though the market in Korea is not fully formed yet. Thus, it is difficult to check its trend to estimated mid- to long-term prospects of the market. In this context, the study predicted demand of the modular housing market by using a non-linear prediction model based on time series analysis. To get the prospects for the modular housing market, the quantity of housing supply was estimated based on the estimated quantity of newly built housings, and assumed that a portion of the supplied quantity would be the demand for modular housings. Based on the assumption of demand for modular housings, several scenarios were analyzed and the prospects of the modular housing market was obtained by utilizing the non-linear prediction model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.451-454
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• 2014

Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Journal of Labour Economics
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• v.37 no.1
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• pp.87-116
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• 2014

Forecasting of short-term employment fluctuations provides a useful tool for policy makers in risk managing the labor market. Following the process of producing the composite leading indicator for macro economy, the paper develops the employment leading indicator(ELI) for the purpose of short-term forecasting non-farm payroll employment in private sectors. ELI focuses on early detecting the point of time and the speed in phase change of employment level.

• 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.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.176-180
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• 2001

In Korea, the mobile telecommunication service market is expanding rapidly and becoming more competitive. For service providers in such a dynamic environment, it is very important to accurately forecast demand including market potential in order to work out marketing strategies. In this paper, we suggest a general approach to forecast the market potential using a multinomial logit model, which is applied to individual-level market survey data. Then we develop a dynamic market potential model that can adapt to changes in the external environment without requiring further market survey. The proposed model is applied to the mobile telecommunication service market in Korea.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1015-1022
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• 2009

Currently, Korean real estate market has experienced cooling down of the business because of the global economic crisis which resulted from the subprime mortgage lending practice. In response, the Korean government has enforced various policies at the base of deregulating real estate speculation, such as increasing Loan to value ratio (LTV) in order to stimulate housing demand and supply. However, these policies seemed to result in deep confusion in the Korean housing market. Furthermore, analysis for housing market forecasting, especially international financial crisis on Korean real estate market, has been partial and fragmentary, therefore comprehensive solution and systematical approach is required to analyze the real estate and real estate financial market including causal nexus between market determining factors. In an integrated point of view, applying the system dynamics modeling, the paper aims at proposing Korean Real Estate and Mortgage market dynamics models based on fundamental principles of housing market determined by supply and demand. We also find the impact of deregulation policies focusing on mortgage loan which is the main factors of policies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.495-498
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• 2007

Recently, as REID Tag and Reader has been attached to, and wireless internet has been added to a mobile phone, the commercialization of Mobile RFID Service to obtain necessary information on daily life and use various applications by using mobile communication infra is drawing nearer. A new returns by Mobile RFID Service can be expected, however, the exact demand forecasting for the Mobile RFID Service is essential to induce mass investment from related communication enterprises. This study tries to get a foothold in enlarging the investment from related communication enterprises through demand forecasting for the Mobile RFID Service and to be helpful to the decision on their investment by predicting the demand on the service various Mobile RFID Service Models.

• Journal of the Korean Society of Costume
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• v.31
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• pp.33-46
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• 1997

The purpose of this study was to investigate color planning method for apparel fashion de-sign and to present the method of analysis of green color. Theoretical backgrouds of color planning for fashion design were scrutinized by documentary studies Fashion color planning has been developed through 4 steps: analysis of color environment analysis of color psy-chology presentation of coordination appli-cation to fashion design. Green color environment consisted of mar-ket informations and forecast informations The former were collected by color samples which were used for women's apparel of national brands from '93 spring/summer to '96 spring/summer and the latter were analyzed by fashion forecasting books. Green color psy-chology was investigated through the docu-mentary studiess. image of green color and these expressed in fashion were revealed through documentary studies. The results of this study were as follow: 1. 117 green color samples were collected from domestic womens brand. The character-istic of samples were the yellow green in hue and pale light bright in tone. forecast infor-mation was collected through fashion forecasting books from abroad and adaption of forecast information was investigated by mak-ing a comparison forecasting information be-tween market information. In consequence national market colors reflected the forecast information in concurrence with the character-istic colors of national women's apparel. 2. Affirmative images of green were nature youth health and abundance and negative images were extraordinary misfortune wind-fall. in these images nature youth and health were mostly used in fashion.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.471-486
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• 2014

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.