• The Journal of the Korea Contents Association
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• v.8 no.8
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• pp.19-27
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• 2008

The advance of IT brings various IT equipment which functions as creation, application, and distribution of digital contents, and its demand is increased in the market. As the world IT equipment market has grew steadily, some companies think of it as a good opportunity. But there is a entry barrier like IT Capabilities to the late movers. So some participate in the market, forming the technology alliance with a advanced company. Ironically, the market system set companies' partnership into rivalry. In this context, our study focused on strengthening late mover's competitiveness under the technology alliance. And we conducted the case study concerning the technology alliance, and showed a strategical implications. As a result, we found some challenges for late mover; price policy making by scientific demanding forecasting, preparatory research and management for brand identity and efficient contact points for customer management. We hope that results of the study will influence the development of digital contents industry.

• Journal of the Korean Society for Railway
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• v.18 no.4
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• pp.391-400
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• 2015

This research used a survey of 350 staff members working at Korail with the purpose of analyzing influence factors for the railway tourism policy for job satisfaction; results were presented for the proposed factor. First, we selected the influence factor through precedent research related to the railway tourism policy. Second, the selected influence factor and the extent of satisfaction were used to determine whether or not any kind of difference existed according to individual attributes of the railway employees. Finally, we analyzed what the influence factor was between the category factor and the sub-category factor. In conclusion, it was found that government subsidy had a meaningful correlation with infrastructure expansion and the improvement of the railway business as it is connected to tourism efficiency. Human resources have a meaningful correlation with the needs of educational institutions and the retaining of talent. Railway tourism production has a meaningful correlation with railway tour production as it is conducted to satisfy tourists and the consortium. The shift of viewpoint has a meaningful correlation with the escape from the peace-at-any price principle and demand forecasting.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.3
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• pp.133-142
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• 2012

Cournot model is one of representative models among many game theoretic approaches available for analyzing competitive market models. Recent years have witnessed various kinds of attempts to model competitive electricity markets using the Cournot model. Cournot model is appropriate for oligopoly market which is one characteristic of electric power industry requiring huge amount of capital investment. When we use Cournot model for the application to electricity market, it is prerequisite to assume the downward sloping demand curve in the right direction. Generators in oligopoly market could try to maximize their profit by exercising the market power like physical or economic withholding. However advanced electricity markets also have demand side bidding which makes it possible for the demand to respond to the high market price by reducing their consumption. Considering this kind of demand reaction, Generators couldn't abuse their market power. Instead, they try to find out an equilibrium point which is optimal for both sides, generators and demand. This paper suggest a quantitative analysis between market variables based on econometrics for estimating demand responses in smart grid environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.108-118
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• 2018

Recently, agricultural sector apply ICT technology such as Smart Farm to pursue innovation in the changing situation that is emerging as the fourth industrial revolution. However, this innovation requires techniques for forecasting and analyzing in various data bases and spatial information provides such infrastructure data. In this study, the cultivation area of Chinese cabbage, radish, garlic, onion, and red pepper were calculated and analyzed by year. The purpose of this analysis is to cope with sudden changes in vegetable crops and changes in cultivated area caused by weather changes to supply and demand of major vegetables and price instability. As a result of this study, spatial information based on time series information of vegetable complex will be used as efficient agricultural environment observation data, as well as interpretation of various spatial ranges such as the estimation of cultivation area using remote sensing.

• Smart Media Journal
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• v.10 no.2
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• pp.16-21
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• 2021

Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.203-214
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• 2022

A model is developed for estimating optimal cultivation areas for apples to maximize the total profit of apple farming by region, focusing on Gyeongsangbuk-do, Chungchungbuk-do, Gyeongsangnam-do, and Jeonllabuk-do in Korea. Comparing the current cultivation areas to the optimal areas according to the model estimation during the period 1999 - 2019, the former has exceeded the latter since 2015 in all regions except for Jeonllabuk-do. This result stems from a lack of the regulation of production quantity among apple producers' regional organizations. Accordingly, the decreasing rate of the market price was greater than the increasing rate of the production quantity, and the total profit of apple farming has fallen in conjunction with increasing agricultural wage rates. Therefore, in order to reverse the current decreasing trend in the profits of apple farming, it is necessary to regulate nationwide apple cultivation areas through regional producers' associations. Ex-ante forecasting for 2019 posits the following regional optimal cultivation areas for maximizing the total income from apple farming. The Gyeongbuk apple producers' association needs to reduce its current cultivation area by 1,089 ha and to maintain 18,373 ha. In the Chungbuk region, current cultivated area should be reduced by 1,027 ha to maintain 2,722 ha, and in the Gyeongnam region, the current cultivated area should be reduced by 582 ha to maintain 2,730 ha. In contrast, the Jeonbuk region needs to increase its current cultivation area by 174 ha and to maintain at a level of 2,872 ha.

• Korea Trade Review
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• v.47 no.4
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• pp.215-231
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• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.