• KIEAE Journal
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• v.16 no.3
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• pp.137-143
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• 2016

Purpose: Apartment housing should conduct a cyclic repair to keep and maintain the building performance since they are constructed. Therefore, the repair plan would be provided for long term period which explains the repair time, items and repair cost. Residents of apartment housing are responsible to pay for the repair activities. For repair cost, residents would reserve the money for repair little by little continuously until the required repair time because the repair cost takes a big burden for residents and lots of money a time. But, there is no systematic approach to provide the long term repair cost because it is no proper forecast of the repair cost to the upcoming repair time. In this study, it aimed at providing the monthly accumulation of the long term repair cost with the survey data in Seoul. Method: For these, the surveyed data are classified into 6 categories and number of data are 1,918. In addition, it developed the repair cost model for the 24 repair works and the cumulation function which is reflected with the each cost model. Result: This study are shown as follows : First, among the various estimation for the repair cost, the power function has a goodness of fit in statistics. Second, the monthly accumulation would be 12,840 won/household in size of $100,000m^2$ management area and $81.7won/m^2$ in size of the 1,000 household number during 40 years.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.585-590
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• 2016

The objective of this study was to develop an export determinant model for laver in the producing district. The annual and monthly amounts of laver products, local price, export price, and foreign exchange rate were included as explanatory variables. The estimation showed that the laver export is influenced more by the long term rather than short term product increase. In addition, as the foreign exchange rate and export price increase, the quantities exported decrease elastically. On the other hand, as the price in the local market increases, the quantities exported decrease non-elastically. Therefore, to enhance the laver exports, it is important to establish infrastructure for long term production increase, forecast and provide information on the export price and foreign exchange rate more accurately.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.4
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• pp.445-451
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• 2012

We developed an age-based spawner-recruit model incorporating environmental variables to forecast stock biomass and recruits of pelagic fish in this study. We applied the model to the Tsushima stock of jack mackerel, which is shared by Korea and Japan. The stock biomass of jack mackerel (Trachurus japonicus) around Korean waters ranged from 141 thousand metric tons (mt) and 728 thousand mt and recruits ranged from 27 thousand mt to 283 thousand mt. We hind-casted the stock biomass to evaluate the model performance and robustness for the period of 1987~2009. It was found that the model has been useful to forecast stock biomass and recruits for the period of the lifespan of fish species. The model is also capable of forecasting the long-term period, assuming a certain climatic regime.

• The Journal of Korean Medicine
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• v.28 no.1 s.69
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• pp.42-50
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• 2007

Objectives . This study was to assess the Korean Traditional Medicine forecast subjects that had been expected to be accomplished by 2005. The result will help the Korean medical society plan far policies and studies on Korean Traditional Medicine. Methods : Assessed targets were 64 subjects (expected to be studied until 2005) of the total 93 subjects from the 'Mid- to Long-Term Forecast and Plan Study for Korean Traditional Medicine'. The subjects were classified into two types : political subjects and research and development (R&D) subjects. These were determined by the quantity and contents of related political reports, political research projects, thesis, patent, placing products on sale, etc. Results :1) 5 items of a total 12 political subjects were accomplished or partially accomplished (41.7%), and 9 items of a total 46 R&D subjects were accomplished or partially accomplished (9.5%). 2) While the accomplishment percentage (accomplished or partial accomplished) in literature arrangement and D/B construction field was 100%, it was under 10% in product or system development field. Thus, it seems that practical subjects were less accomplished than academic subjects. 3) On 8 subjects of 'Forecast Research on Future of Oriental Medicine' which had been performed in Japan, the Korean expected dates when the subjects would be realized were earlier than the Japanese ones, but no subjects were realized. Conclusion · Political and academic subjects weir accomplished more than R&D and practical subjects.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.1-12
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• 2021

Altman's Z-score is used to measure a company's financial health and to predict the probability that a company will collapse within 2 years. It is proven to be very accurate to forecast bankruptcy in a wide variety of contexts and markets. The goal of this study is to use Altman's Z-score model to forecast insolvency in non-financial publicly traded enterprises. Non-financial firms are a significant industry in Malaysia, and current trends of consolidation and long-term government subsidies make assessing the financial health of such businesses critical not just for the owners, but also for other stakeholders. The sample of this study includes 84 listed companies in the Kuala Lumpur Stock Exchange. Of the 84 companies, 52 are considered high risk, and 32 are considered low-risk companies. Secondary data for the analysis was gathered from chosen companies' financial reports. The findings of this study show that the Altman model may be used to forecast a company's financial collapse. It dispelled any reservations about the model's legitimacy and the utility of applying it to predict the likelihood of bankruptcy in a company. The findings of this study have significant consequences for investors, creditors, and corporate management. Portfolio managers may make better selections by not investing in companies that have proved to be in danger of failing if they understand the variables that contribute to corporate distress.

• Korean Journal of Construction Engineering and Management
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• v.25 no.4
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• pp.53-65
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• 2024

During COVID-19 the housing market in Korea experienced the soaring prices, despite the decrease in the economic growth rate. This paper aims to analyze macroeconomic determinants affecting housing prices in Korea during the pandemic and find an appropriate statistic model to forecast the changes in housing prices in Korea. First, an appropriate lag for the model using Akaike information criterion was found. After the macroeconomic factors were checked if they possess the unit root, the dependencies in the model were analyzed using vector autoregression (VAR) model. As for the prediction, the VAR model was used and, besides, compared afterwards with the long short-term memory (LSTM) model. CPI, mortgage rate, IIP at lag 1 and federal funds effective rate at lag 1 and 2 were found to be significant for housing prices. In addition, the prediction performance of the LSTM model appeared to be more accurate in comparison with the VAR model. The results of the analysis play an essential role in policymaker perception when making decisions related to managing potential housing risks arose during crises. It is essential to take into considerations macroeconomic factors besides the taxes and housing policy amendments and use an appropriate model for prices forecast.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.105-117
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• 2022

This study analyzes the effects between stock returns and interest rate spread, difference between long-term and short-term interest rate through the polynomial linear regression analysis. The existing research concentrated on the business forecast through the interest rate spread focusing on the US market. The previous studies verified the interest rate spread based on the leading indicators of business forecast by moderating the period of long-term/short-term interest rates and analyzing the degree of leading. After the 7th reform of composite indices of business indicators in Korea of 2006, the interest rate spread was included in the items of composing the business leading indicators, which is utilized till today. Nevertheless, there are a few research on stock returns of each industry and interest rate spread in domestic stock market. Therefore, this study analyzed the stock returns of each industry and interest rate spread targeting Korean stock market. This study selected the long-term/short-term interest rates with high causality through the regression analysis, and then understood the correlations with each leading period and industry. To overcome the limitation of the simple linear regression analysis, polynomial linear regression analysis is used, which raised explanatory power. As a result, the high causality was verified when using differences between returns of corporate bond(AA-) without guarantee for three years by leading six months and call rate returns as interest rate spread. In addition, analyzing the stock returns of each industry, the relation between the relevant interest rate spread and returns of the automobile industry was the closest. This study is significant in the aspect of verifying the causality of interest rate spread, business forecast, and stock returns in Korea. Even though it could be limited to forecast the stock price by using only the interest rate spread, it would be working as a strong factor when it is properly utilized with other various factors.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1758-1761
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• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.1-49
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• 2006

Considering the fact that households' demographic characteristics affect consumption decision, it is conjectured that rapid demographic changes would lead to a substantial change in the composition of private consumption expenditure. This paper estimates the demand functions of various consumption items by applying the Quadratic Almost Ideal Demand System(QUAIDS) model to Household Income and Expenditure Survey data, and then provides a long-term forecast of the composition of household consumption expenditure for 2005-2020. The paper shows that Korea's consumption expenditure will maintain the recent years' rapid change, of which a considerable portion is due to rapid demographic changes. Results of the paper can be utilized in forecasting the change in the industrial structure of the economy, as well as in firms' investment planning.