• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.59-71
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• 2016

This study empirically examines the dynamic specification of the ship price model based on a vector autoregressive model and data covering from January 2000 to October 2014. Our results are summarized as follows: first, the relationship between ship price and interest rate shows significantly negative and the relationship between ship price and freight rate shows positive. It provides consistent implication that ship price depends on interest rate and freight rate under the dynamic Gordon model. Second, we apply an impulse response analysis to ship price and find the responses of the ship price from both factors, interest rate and freight rate, which affect during seven periods approximately. Finally, the results of a variance decomposition indicate that freight rate is more important than interest rate on the ship price.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.403-410
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• 2010

NDVI(Normalized Difference Vegetation Index), which is broadly used as short-term data composite, is an important parameter for climate change and long-term land surface monitoring. Although atmospheric correction is performed, NDVI dramatically appears several low peak noise in the long-term time series. They are related to various contaminated sources, such as cloud masking problem and wet ground condition. This study suggests a simple method through harmonic analysis for reducing NDVI noise using SPOT/VGT NDVI 10-day MVC data. The harmonic analysis method is compared with the polynomial regression method suggested previously. The polynomial regression method overestimates the NDVI values in the time series. The proposed method showed an improvement in NDVI correction of low peak and overestimation.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.517-525
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• 2013

Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.513-519
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• 2018

The objective of this study was to examine the trends on social network services. The abstracts of 308 articles were extracted from web of science database published between 1994 and 2016. Time series analysis and topic modeling of text mining were implemented. The topic modeling results showed that the research topics were mainly 20 topics: trust, support, satisfaction model, organization governance, mobile system, internet marketing, college student effect, opinion diffusion, customer, information privacy, health care, web collaboration, method, learning effectiveness, knowledge, individual theory, child support, algorithm, media participation, and context system. The time series regression results indicated that trust, support satisfaction model, and remains of the topics were hot topics. This study also provided suggestions for future research.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.720-722
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• 2019

학령인구의 감소에 따른 대학 구조개혁에 대한 경쟁력 강화 방안의 일환으로 각 대학에서는 다양한 설문조사 및 만족도 조사를 시행하고 있다. 그러나, 대부분의 대학은 설문조사의 통합 관리체계 및 운영 방법의 효율성 그리고 활용 방법에 대한 고려 없이 업무별, 시스템별, 다양한 인터넷 무료 설문조사 시스템을 사용하고 있어 체계적이고 효율적인 설문 관리가 어렵다. 본 논문에서는 대학 내에서 운영되는 모든 설문조사 업무를 통합 관리할 수 있는 권한 모델을 설계하고, 자료를 체계적으로 저장할 수 있는 구조를 만들어, 축적된 데이터에 대한 시계열분석, 상관분석, 회귀분석이 가능한 시스템을 제안한다. 제안된 시스템은 학교의 설문조사 업무를 효율화하고, 대학에 필요한 다양한 분석 방법을 제공하여 대학의 발전에 기여 할 수 있을 것으로 사료된다.

• The Korean Journal of Financial Studies
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• v.6 no.1
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• pp.203-221
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• 2000

본 연구는 선진국들의 주가변동이 우리나라 증시에 미치는 영향, 아시아 각국과 우리나라 주가변동의 상관관계, 우리나라와 이들 국가간 주가변동 상관관계의 시계열적 안정성, 우리나라와 이들 국가간 주가변동 전이의 방향 및 시차에 관하여 분석 하였다. 우리나라 증시 개방시점을 전후하여 1981년 1월$\sim$1991년 12월을 제1기간, 1992년 1월$\sim$1999년 8월을 제2기간으로 한 각국 주가지수의 월별수익률과 1999년 1월 4일$\sim$1999년 8월 24일 기간중 각국 주가지수의 일별수익률을 자료로 상관분석과 회귀분석을 실시한 결과는 다음과 같다. 첫째, 우리나라 증시에 영향을 미친 선진국으로는 제1기간중에는 영국, 일본, 미국의 순이며, 제2기간중에는 전적으로 일본과의 상관관계가 높게 나타났다. 특히 일본과의 상관관계는 제1기간보다도 제2기간중에 더욱 상승하였다. 또한 선진국들 간에는 독일, 영국, 프랑스 3국간 주가변동의 상관관계가 높게 나타났다. 둘째, 아시아 각국과 우리나라의 상관관계분석에서는 태국, 인도네시아, 말레이시아와는 상관관계가 높게 나타났으나, 대만, 싱가폴과는 상관관계가 비교적 낮게 나타났다. 그리고 아시아 각국간에는 홍콩, 말레이시아, 태국, 인도네시아 국가들의 상호간 상관관계가 높게 나타났다. 셋째, 상관관계의 시계열 안정성에 관한 분석에서는 1995년 이전까지는 전체적으로 우리나라와 각국간 주가변동의 상관관계가 낮거나 국가별로 매우 불안정한 상태를 나타내고 있으나, 1996년 이후부터는 상관관계가 점점 높아지는 추세에서 1998년도에만 우리나라의 IMF충격으로 인하여 일부국가들과의 상관관계가 일시적으로 낮아진 상태를 보이고 있다. 개별국가별로는 우리나라와 일본과의 상관관계가 지속적으로 높아지고 있으며 태국, 인도네시아와는 IMF사태이후 크게 높아진 것으로 나타났다. 넷째, 주가변동방향 및 시차분석에서는 미국, 영국, 독일의 주가변동이 1일의 시차를 두고 우리나라에 영향을 미치고 있으며, 일본을 비롯한 동남아 각국과 우리나라간에는 주가변동에 시차가 없는 것으로 나타났다. 그러나 각국간 표준시차 및 거래소 거래시간을 고려하면 미국, 영국, 독일의 경우에도 그 시차는 1일이내이거나 거의 시차가 없는 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.335-350
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• 1998

X11ARIMA is established on the basis of X11 which is one of smoothing approach in time series area and this procedure was introduced by Bureau of Census of United States and developed by Dagum(1975). This procedure had been updated and adjusted by Dagum(1988) with 174 economic index of North America and has been used until nowadays. Recently, X12ARIMA procedure has been studied by William Bell et.al. (1995) and Chen. & Findly(1995) whose approaches adapt adjusting outliers, Trend-change effects, seasonal effect, arid Calender effect. However, both of these procedures were implemented for correct adjusting the economic index of North America. This article starts with providing some appropriate and effective ARIMA model for 102 indexes produced by national statistical office in Korea; which consists of production(21), shipping(27), stock(27), and operating rate index(21). And a reasonable smoothing method will be proposed to reflect the specificity of Korean economy using several moving average model. In addition, Sulnal(lunar happy new year) and Chusuk effects will be extracted from the indexes above and both of effects reflect contribution of lunar calender effect. Finally, we will discuss an alternative way to estimate holiday effect which is similar to X12ARIMA procedure in concept of using both of ARIMA model and Regression model for the best fitness.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.