• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.327-335
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• 2016

The People's Republic of China has vigorously been pursuing the internationalization of the Chinese Yuan or Renminbi after the financial crisis of 2008. In this view, an abrupt increase of use of the Chinese Yuan in the onshore and offshore markets are important milestones to be one of important currencies. One of the most frequently used methods to forecast volatility is GARCH model. Since a prediction error of the GARCH model has been reported quite high, a lot of efforts have been made to improve forecasting capability of the GARCH model. In this paper, we have proposed MLP-GARCH and a DL-GARCH by employing Artificial Neural Network to the GARCH. In an application to forecasting Chinese Yuan volatility, we have successfully shown their overall outperformance in forecasting over the GARCH.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.1-18
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• 2018

The purpose of this study is to analyze sensitivity of forest ecosystem to climate change using spatial analysis methods focused on 6 national parks. To analyze, we constructed MODIS NDVI and temperature of Korea Meteorologic Administration based on 1km spatial resolution and 16 days. And we conducted time-series and correlation analysis using MODIS NDVI and temperature. A most sensitive region to climate change is Jirisa National Park(r=0.434) and Seoraksan National Park(r=0.415), there is the highest mean correlation coefficient. The sensitivity of forest ecosystem varied according to habitat characteristics and forest types in national park. In Abies koreana of Hallsan Nation Park, temperature has raised, but NDVI has decreased. these results will be based data of climate change adaption policy for protecting forest ecosystem.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.199-199
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• 2023

최근 기후변화의 영향으로 가뭄, 홍수 등 재해의 발생 빈도 및 강도가 증가하고 있다. 미래에는 온실가스 배출량의 증가로 극한 기상현상은 더욱 심화될 전망이다. 이러한 위험에 효율적으로 대비하기 위해 기후변화 시나리오를 고려하여 미래를 전망하는 것은 매우 중요하며, 최근 연구자들은 불확실성을 고려하기 위해 다양한 시나리오를 적용하고 있는 추세이다. 다만, 기후변화 시나리오를 입력자료로 하여 분석을 수행하는 경우, 새로운 기후변화 시나리오가 생성될 때 기존 기후변화 영향 평가는 무의미해지며, 기존 결과의 신뢰도 또한 감소하게 된다. 지금까지 사용된 시나리오 기반 접근법의 한계를 보완하여 시나리오 중립(Scenario Neutral, SN) 접근법이 개발되었고, 이는 다양한 기후변화 시나리오에 대한 시스템의 반응을 평가하는데 유용하다. 본 연구에서는 시나리오 중립 접근법을 활용하여 가뭄 위험도를 분석하였으며, 이를 위해 금강 유역 내 용담댐 유역을 대상으로 분석을 수행하였다. 입력자료로는 용담댐 유역의 1966~2020년 일단위 강수량 자료를 사용하였고, 문헌 조사를 통해 미래 기후변화에 따른 강수량 변화 추이를 파악하였다. 연평균 강수량의 증가와 여름 강수량의 증가를 기준으로 삼아 증가 비율에 따른 노출 공간을 생성했으며, 목표 변화에 따른 교란된 시계열을 도출해냈다. 이후, 각각의 시계열에 대한 이변량 가뭄빈도분석을 수행하여 재현기간을 산정한 뒤, 목표 변화에 따른 위험도를 비교하였다. 그 결과, 연평균 강수량과 여름 강수량이 현재와 유사한 경우 100년 빈도 가뭄이 발생할 확률은 0.84, 연평균 강수량의 증가가 110%, 여름 강수량의 증가가 115%일 경우 100년 빈도 가뭄이 발생할 확률은 0.79이었다. 추후 실제 미래 기후변화 시나리오를 적용하여 기준치에 따른 만족도를 분석한다면, 가뭄 대응에 유용한 의사결정 도구로 활용될 수 있을 것이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.210-216
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• 2015

It is well known that the structure of brain and consciousness of human have a phenomena of complex system. The human emotion have a many kind. The love is one of human emotion, which have been studied in sociology and psychology as a matter of great interested thing. In this paper, we consider a same and different time delay in love equation of Romeo and Juliet. We represent a behavior of love as a time series and phase portrait, and analyze the difference of behaviors between a same and different time delay.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.133-144
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• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.237-252
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• 2013

An estimation model for premiums and components is essential to determine reasonable insurance premiums. In this study, we introduce diverse models for the estimation of property damage premiums(premium, depth and frequency) that include a regression model using a dummy variable, additive independent variable model, autoregressive error model, seasonal ARIMA model and intervention model. In addition, the actual property damage premium data was used to estimate the premium, depth and frequency for each model. The estimation results of the models are comparatively examined by comparing the RMSE(Root Mean Squared Errors) of estimates and actual data. Based on real data analysis, we found that the autoregressive error model showed the best performance.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.347-356
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• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.861-866
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• 2022

Seoul introduced the shared bicycle system, "Seoul Public Bike" in 2015 to help reduce traffic volume and air pollution. Hence, to solve various problems according to the supply and demand of the shared bicycle system, "Seoul Public Bike," several studies are being conducted. Most of the research is a strategic "Bicycle Rearrangement" in regard to the imbalance between supply and demand. Moreover, most of these studies predict demand by grouping features such as weather or season. In previous studies, demand was predicted by time-series-analysis. However, recently, studies that predict demand using deep learning or machine learning are emerging. In this paper, we can show that demand prediction can be made a little better by discovering new features or ordering the importance of various features based on well-known feature-patterns. In this study, by ordering the selection of new features or the importance of the features, a better coefficient of determination can be obtained even if the well-known deep learning or machine learning or time-series-analysis is exploited as it is. Therefore, we could be a better one for demand prediction.