• Journal of Korean Society of Rural Planning
• /
• v.14 no.3
• /
• pp.1-9
• /
• 2008

In this study, by reflecting flow of age, possibility of new theme park development as private investments business based on source that is farming village that is not tried to before is verified and by analyzing potential of the site, effectiveness of new theme park development is examined. "Nonghyup Anseong Meadow Anseong-si Gyeonggi-do" is selected as researched site where accessibility is good as there is near to National Capital region and nature condition is also good. Demands are forecasted using visiting intention and realizing index through analogical method and by analyzing existing data related with increase of tourism business that people can experience English village and increasing demand of experiencing farming region tourism demands are forecasted. The results are at below. First, As average expenditure per one person is 52,209 won that is shown in result of survey, if multiplying increasing rate of price and the number of visiting people that is optimistic forecasting figure, the whole expenditure of visitors per one year is from 10.54 billions to 13.85 billions won. Second is potential power of demand aspects. Potential power of that theme park was re-examined through demands forecasting analysis through survey. Experiencing farming regions theme park business that is informed through analysis of potential power of development and demand aspects has value to invest as new business based on farming regions sources, as a result of searching through diverse aspects such as tourism, economy, public interest and cultural aspect and so on.

• Asian Journal of Innovation and Policy
• /
• v.9 no.2
• /
• pp.187-206
• /
• 2020

Under the on-going evolution of the COVID-19 pandemic, estimating the economic impact of the pandemic is highly uncertain and challenging. This situation makes it difficult for policymakers, governors, and economic entities to formulate appropriate responses and decision makings. To provide useful information about the effect of the COVID-19 pandemic on the Korean economy, this study examined macroeconomic impact analysis stemming from the pandemic shocks with different scenarios for the Korean economy. Based on three scenarios using the growth rate of 2020 GDP and consumer expenditure patterns, the 2021 GDP by industry sector was forecast with two new approaches. First, the recovering process of the Korean economy from the shock was analyzed by applying a Flex-IO method. Second, a new forecasting approach combined with an IO coefficient matrix was applied to forecast the future GDP changes. The findings of this study are summarized as follows: First, the total GDP growth rate under the Pessimistic Scenario demonstrates less rebound from the shock than that of the Base Scenario. Second, agriculture, culture, and tourism-related sectors that are suffering from the severe losses of COVID-19 showed lower resilience than other different industries. Third, information and communications technology (ICT) industry maintains a stable growth trend and is expected to take the leading role for the Korean economy in the post-COVID-19 and the Industry 4.0 eras. The findings deliver that it needs to analyze how government expenditure responding the shock into the forecasting model, which can be more useful and reliable to simulate the resilience from the pandemic.

• Journal of Fashion Business
• /
• v.15 no.6
• /
• pp.176-203
• /
• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.12
• /
• pp.109-114
• /
• 2020

The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia's growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.

• Korean Journal of Construction Engineering and Management
• /
• v.7 no.2 s.30
• /
• pp.138-147
• /
• 2006

The objective of this research is to explore a more precise forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, 1) the choice of the numbers of referring projects and 2) the better selection among three levels ? which include a 20-work package level, a 7-major work package level, and a total sum level analysis, were investigated in detail. It is concluded that selecting similar projects at $12{\sim}19%$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.3139-3153
• /
• 2018

The purpose of this study is to analyze the gap of private tutoring expenditure using data from 2001 to 2016 of Korean labor and income panel study (KLIPS). The final analysis target is about 1,300 to 1,800 households nationwide who participated in the Korean labor panel survey in each year. As a result of the analysis, the expenditure of private tutoring expenditure has been continuously increased since 2001, and it is analyzed that there is a large gap in expenditure of private tutoring even in the comparison of groups divided by the quintile. The spending gap on private tutoring expenditure in the first and fifth quintiles has increased steadily, reaching 11 times in recent years. By forecasting the polarization of private tutoring expenditure since 2016 using the Brown's smoothing method, it is highly likely that the polarization of private tutoring expenditure will be further expanded. The implications for preparing an alternative educational policy and suggestions of conducting a follow-up study for private tutoring expenditure gaps were also discussed.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.253-259
• /
• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Journal of Distribution Research
• /
• v.6 no.2
• /
• pp.41-64
• /
• 2002

Since its market opening in the year 1996, Korea's retail sector has witnessed the emergence of various new retail formats such as discount stores and Internet shopping malls. Given the competition among various retail formats, it is needed to analyze the previous trends and to measure the future potential of the market with more careful economic models. Using Time Series Analysis on Korean economy and distribution industry, we aim to economic models to follow the trends and to measure the future growth of competing retail formats such as department stores, discount stores and convenience stores. We have found that the growth of department stores, convenience stores and specialty store format is very closely related with the private consumption expenditure. On the other hand, private consumption expenditure is not a good variable to explain the growth of discount stores and the supermarket sector. Following an extensive data analysis, three year forecasting of Korean distribution market including six different retail sectors is proposed. In addition, several discussion points including statistical classification of retail formats are argued.

• International Journal of Industrial Entomology and Biomaterials
• /
• v.8 no.1
• /
• pp.17-26
• /
• 2004

Pest monitoring through field surveys and surveillance helps in forecasting the population build up of pest. It reduces the load of pesticides application and forms the basis of Integrated Pest Management in sericulture. Common sampling techniques for quantifying pest populations and damage caused by them are reviewed emphasizing the need for quick and simple sampling methods. Various direct and indirect sampling methods for establishing pest populations are discussed and methods have been discussed to use indirect sampling method under IPM programme in sericulture. The use of pheromone lures and traps forms one of the important ingredients of integrated pest management, which calls for integration of all available methods in a cost effective and environmental friendly manner offering consistent efficacy. Silk-worms feed on the variety of silk host plants and spin cocoons. Each silk host plant is attacked in the field by number of insect pest species. Several pests are common to mulberry, tasar, oak tasar, muga and eri host plant but pest status and seasonal abundance differs from each crop. The key pests are serious perennially occurring persistent species which cause considerable yield loss every year on large areas and require control measure. Regular occurrence of minor pest is noticed but sudden increase in its population is not known. The occasional pests are sporadic but potential causing sufficient damage. Silk losses due to attack of all the pests have not been calculated. However, information on pest biology and ecology, and control practices being practiced is available but the period of outbreak of major pests and predators on silkworms and its host plant needs to be reinvestigated. Pest and predators forecasting based on surveillance information may provide an opportunity to minimize the losses, particularly to reduce expenditure involved in pest management.

• Journal of Information Technology Applications and Management
• /
• v.21 no.3
• /
• pp.1-17
• /
• 2014

In this study, various characteristics of R&D related economic variables were studied to analyze complexity of science and technology activities in Korea, as reliance of R&D activities of the private sector is growing by the day. In comparison to other countries, this means that it is likely to be fluctuated by economic conditions. This complexity characteristic signifies that the result of science and technology activities can be greatly different from the anticipated results - depending on the influences from economic conditions and the results of science and technology activities which may be unpredictable. After reviewing the results of 17 variables related to science and technology characteristics of complex systems intended for time-series data - in the total R&D expenditure, and private R&D expenditure, numbers of SCI papers, the existence of chaotic characteristics were. using Lyapunov Exponent, Hurst Exponent, BDS test. This result reveals science and technology activity of the three most important components in Korea which are; heavy dependence on initial condition, the long term memory of time series, and non-linear structure. As stable R&D investment and result are needed in order to maintain steady development of Korea economy, the R&D structure should be less influenced by business cycles and more effective technology development policy for improving human resource development must be set in motion. And to minimize the risk of new technology, the construction of sophisticated technology forecasting system should take into account, for development of R&D system.