• Journal of Industrial Convergence
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• v.15 no.1
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.498-512
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• 2023

The traditional newspaper industry faces the opportunities and challenges of industry transformation and integration with new media. Consequently, the catalogs of newspapers and magazines are also updated. In this study, necessary information on catalogs was obtained and used to analyze the overall development trend of the newspaper industry. A word frequency analysis was then performed on the introduction and product categories of the catalogs, and the content and types of newspapers and magazines were examined. Furthermore, related factors such as price, number of pages, publishing frequency, and best-selling status were analyzed; the correlation among factors affecting best-selling status was also explored. Subsequently, each element and a combination of elements were used to generate a dataset, build three classification models, and analyze the accuracy of predictions of whether newspapers sold well under other circumstances. The experimental results showed that price is the most critical factor affecting the best-selling status of newspapers and magazines. Publishing frequency and the number of pages were also found to be significant indicators that impact people's subscription choices. Finally, a competitive strategy regarding content, price, quality, and positioning was developed.

• International Commerce and Information Review
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• v.16 no.3
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• pp.73-96
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• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.317-323
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• 2016

Big Data is now seen as a major asset in the company's competitiveness, its influence in the future is expected to grow. Companies that recognize the importance are already actively engaged with Big Data in product development and marketing, which are increasingly applied across sectors of society, including politics, sports. However, lack of knowledge of the system implementation and high costs are still a big obstacles to the introduction of Big Data and systems. It is an objective in this study to build a Big Data system, which is based on open source Hadoop and Hive among Big Data systems, utilizing POS sales data of small and medium-sized offline markets. This approach of convergence is expected to improve existing sales systems that have been simply focusing on profit and loss analysis. It will also be able to use it as the basis for the decisions of the executive to enable prediction of the consumption patterns of customer preference and demand in advance.

• KDI Journal of Economic Policy
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• v.26 no.1
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• pp.3-33
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• 2004

This paper investigates the effects of inflation on real estate prices, particularly the discrepancy between the sales and chonsei prices of housing, in an economy in which real interest rates are secularly declining due to the fall in capital productivity. When real interest rates fall, real estate prices rise relative to chonsei prices, and thus the well-known adverse effect of inflation, or the discrepancy between the value of financial assets (or chonsei principal) and the value of real assets (or real estate), is aggravated although the monetary authority maintains the same rate of inflation. This theoretical prediction can help explain the trend of the ratio of apartment sales prices to chonsei prices. That is, the stabilization of inflation relative to real interest rates appears to have contributed to the secular stabilization of this ratio in the 1990s, while the fall in real interest rates appears to have led to the rise of this ratio since 2001.

• Fire Science and Engineering
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• v.32 no.6
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• pp.55-62
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• 2018

To improve the prediction result with enhanced reliability of domestic Performance-Based Design (PBD), actual scale fire tests were carried out on products made of plastics from sales facility combustibles. The commercial buildings were separated into single and multiple combustibles for the experimentation of fire spread caused by the sales shelves where the various combustible materials are displayed. A according to the maximum heat release rate, exposed area and weight of the combustible material, the results revealed a linear relationship of as 93% and 89%. In addition, analysis of the gas concentrations for various combustibles showed that $CO_2$ has a linear relationship, whereas the CO concentration indicated exponential function. These results can be applied to reliable fire source information in PBD of plastic fire source in commercial buildings. This may be applied as fire source information representative of a plastic fire in commercial buildings through additional experiment using the area of the shelf in actual commercial buildings.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• The Journal of Bigdata
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• v.5 no.1
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• pp.109-124
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• 2020

In Korea, despite much interest in real estate, it is not easy to predict prices. Because apartments are both residential spaces and investment materials. Key figures affecting the price of apartments vary widely, and there are also regional characteristics. This study was conducted to derive the factors and characteristics that affect the sale price of apartments in S City, Gyeonggi-do. In general, people diagnose that better subway accessibility leads to higher apartment sales price. Nevertheless, in the case of S City, the price was slightly lower as it was closer to Line 1, but the higher the subway accessibility at Shinbundang Line, the higher the price. The five-year average of government bonds and the price were inversely related, and it was found to be proportional to the M2 balance and the price. The floor area ratio and the total number of parking lots had a great influence on the price, and the presence of department stores and discount marts within 1.5 km were the most important factors in the area of cultural aspect.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.