• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.257-267
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• 2020

We introduce multivariate exponential smoothing models based on a vector innovations structural time series framework. The models enable us to exploit potential inter-series dependencies to improve the fit and forecasts of multivariate (vector) time series. Models are applied to forecast the exchange rates of the UK pound (UKP) and US dollar (USD) against the Korean won (KRW) observed on monthly basis; subseqently, we compare their performance with alternative models. We observe that the multivariate exponential smoothing models are superior to alternatives.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1129-1145
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• 2016

Functional neuro-connectivity is one of the main issues in brain science in the sense that it is closely related to neurodynamics in the brain. In the paper, we choose fMRI as a main form of response data to brain activity due to its high resolution. We review methods for analyzing functional neuro-connectivity assuming that measurements are made on physiological responses to neuron activation. This means that we deal with a state-space and measurement model, where the state-space model is assumed to represent neurodynamics. Analysis methods and their interpretation should vary subject to what was measured. We included analysis results of real fMRI data by applying a high-dimensional autoregressive model, which indicated that different neurodynamics were required for solving different types of geometric problems.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• International Area Studies Review
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• v.20 no.1
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• pp.53-78
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• 2016

This study is to analyze the effects of both the bilateral FTA and a home and its trade partner's FTAs on the trade with 62 country-pair panel data over the period of 2003-2013 using the gravity model and the spatial autoregressive model. First, the study analyzes how the bilateral FTAs affect the trade using the gravity model and the spatial model. Next, the article analyzes how the home and its trade partners' FTAs affect their trade using only the spatial model under controlling the bilateral FTA. The empirical results are summarized as the followings: first, the spatial mode fits well more than the gravity model in analyzing the relationship between the bilateral FTA and trade. It implies that the spatial spillover effect of FTA is important in the international trade with FTA. Second, the bilateral FTA plays a role in expanding the trade between or among the FTA members as proved by the previous studies. Third, the more the home and its trade partners' FTAs, the more the bilateral trade are extended. Fourth, with the bilateral FTAs, as the home and its trade partners enter into more FTAs, the bilateral trade reduces due to trade diversion effects. In conclusion, this study provides a political implication that in order to increase the trade volume, a country enters into as many FTAs as possible because the effects of the bilateral FTAs would decrease.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.262-262
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• 2008

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.345-352
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• 2006

현 기상의 시점에서 강수 확률 예측을 위해 가장 적절한 모형은 공간적 종속성과 시간적 종속성을 고려한 모형이 선택되어져야 한다. 보통 마크프 연쇄 모형과 예보인자를 이용하는 회귀 모형이 모두 고려된 모형을 사용한다. 본 논문에서는 강수 형태를 세 개의 상태로 나눈 경우, 즉 맑은 경우, 흐린 경우, 비온 경우로 나누어 마코프 로지스틱 회귀모형을 세우고 강수확률을 예측 할 수 있도록 하였다. 또한 서울 지역의 강수 자료를 이용하여 기존의 마코프 회귀모형과 마코프 로지스틱 회귀모형을 서로 비교하여 실제적 적용 문제를 다루었다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.2
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• pp.161-169
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• 2004

In this study, a similarity rule is thoroughly discussed for tunnelling model tests which can simulate actual ground conditions. Based on this prior consideration, theoretical works are performed to simulate a real ground condition into a gravitational field with a similarity. A process is arranged to determine a lining condition in laboratory tests for a sandy ground tunnel.

• Journal of Astronomy and Space Sciences
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• v.10 no.2
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• pp.163-188
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• 1993

On the framework of Tolman spacetime model, the caustics around a local perturbed region in redshift is due to the local expansion rate induced by a local density inhomogeneity in real space. We have compared the caustics in redshift space, which are analytically obtained, with the observed redshift-distance patterns of galaxies which are belonging to Coma and Perseus clusters. For the Abell density distribution model and polytropic density profiles which are well-fitting the optical and X-ray observations, respectively, the size of caustics which is defined by "turnaround radius" of a local density perturbed region should give constraints on the sizes and masses of rich clusters and give also a clue to understand the state of hot X-ray emitting gas.