• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.151-152
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and Stochastic Volatility Models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.835-841
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and stochastic volatility models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제21권1호
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• pp.77-93
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• 2014

This paper focuses on computational contractual distinctions as an explanation for the spread between a forward contract and a similar futures contract which is derived and investigated. We evaluate this spread by constructing a time series model, which was established based on copula functions, and also show that the forward-futures spread is more significant for long maturity.

• Structural Engineering and Mechanics
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• 제54권5호
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• pp.937-953
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• 2015

This paper investigates the load model for single footfall trace of human walking. A large amount of single person walking load tests were conducted using the three-dimensional gait analysis system. Based on the experimental data, Fourier series functions were adopted to model single footfall trace in three directions, i.e. along walking direction, direction perpendicular to the walking path and vertical direction. Function parameters such as trace duration time, number of Fourier series orders, dynamic load factors (DLFs) and phase angles were determined from the experimental records. Stochastic models were then suggested by treating walking rates, duration time and DLFs as independent random variables, whose probability density functions were obtained from experimental data. Simulation procedures using the stochastic models are presented with examples. The simulated single footfall traces are similar to the experimental records.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1437-1440
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• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.

• 응용통계연구
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• 제27권3호
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• pp.475-485
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• 2014

일반화 선형모형(GLM)에 기초한 확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 가장 일반적으로 사용되는 방법인다. 본 논문에서는 다층구조를 이용하여 기존의 GLM weather generator에 공간구조를 소개하였다. 계절별 총강우량의 overdispersion 현상을 효과적으로 제거하기 위해서 smoothing된 계절별 총강우량을 모형에 포함하였고 공간구조를 소개하기 위해서 Stochastic weather generator의 모형계수에 공간구조를 가지는 다변량 정규분포를 가정하였다. 그리고 제안된 공간구조를 가지는 GLM weather generator 모형을 우리나라 76개 지역에서 39년간 측정된 일별 강우량 관측자료에 적용하였다.

• 물과 미래
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• 제14권2호
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• pp.71-79
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• 1981

이 연구는 남한강유역의 월강우량과 월유출량을 추계학적 및 통계학적으로 분석하여 두 개의 시계열 모형식을 설정하였다. 월유출량의 시계별 모의기법은 모의합성하는 월유출량자료로 선형추계학적 모형을 유도하였다. 또한, 월강우량과 월유출량의 시계열모열은 기지 통계학적 변수를 갖는 임의 시계열이라 하여 유도하였으며, 이는 월유출량이 한 변수를 갖는 지수감소곡선의 특성과 이월기간에 대한 통계학적 변수로 전개되었다.