• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.948-951
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• 2014

Road traffic noise is closely related with urban forms and urban components, such as population, building, traffic and land-use, etc. Hence, it is possible to minimize the noise exposure problem depending on how to plan new town or urban planning alteration. This paper provides ways to apply for urban planning in consideration of noise through exposed noise estimation for urban planning alteration. Spatial autoregressive model which explains about 81.4% of road traffic noise from the former paper is used. The simulation results by the spatial statistical model are compared with those by the engineering program-based modeling for 5 small-scaled scenarios of urban planning alteration. The error from the limitation of containing informations inside the grid cell and the difficulties of reflecting acoustic phenomena is existed. Nevertheless, in the stage of preliminary design, the use of the statistical models that have been estimated well is useful in time and economically.

• 대한산업공학회지
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• 제36권3호
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• pp.203-211
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• 2010

This study examines a cointegrated vector autoregressive (VAR) model where parameters are subject to switch across the regimes in the term structure of interest rates. To employ the regime switching framework, the Markov-switching vector error correction model (MS-VECM) is allowed to the regime shifts in the vector of intercept terms, the variance-covariance terms, the error correction terms, and the autoregressive coefficient parts. The corresponding approaches are illustrated using the term structure of interest rates in the US Treasury bonds over the period of 1958 to 2009. Throughout the modeling procedure, we find that the MS-VECM can form a statistically adequate representation of the term structure of interest rate in the US Treasury bonds. Moreover, the regime switching effects are analyzed in connection with the historical government monetary policy and with the recent global financial crisis. Finally, the results from the comparisons both in information criteria and in forecasting exercises with and without the regime switching lead us to conclude that the models in the presence of regime dependence are superior to the linear VECM model.

• 응용통계연구
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• 제35권5호
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• pp.645-655
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• 2022

본 논문에서는 R에서 시계열 자료 예측을 위한 자동화 함수에 대하여 고찰하고 그 예측 성능을 비교합니다. 대표적인 시계열 예측 방법인 지수 평활 모형과 ARIMA (autoregressive integrated moving average) 모형을 대상으로 하였으며, 이들의 모형화 및 예측 자동화를 가능하게 하는 R의 4가지 자동화 함수인 forecast::ets(), forecast::auto.arima(), smooth::es()와 smooth::auto.ssarima()를 대상으로 하였습니다. 이들의 예측 성능을 비교하기 위하여 3,003가지의 시계열로 구성되어 있는 M3-Competition자료와 3가지의 정확성 척도를 사용하였습니다. 4가지 자동화 함수는 모형화의 다양성 및 편리성, 예측 정확도 및 실행 시간 등에서 각자 장단점이 있음을 확인하였습니다.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• 디지털융복합연구
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• 제13권8호
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• pp.267-279
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• 2015

본 연구는 초등학생의 휴대전화 중독적 사용과 학습활동의 종단적 관계를 분석함으로써 (1)시간의 경과에 따른 휴대전화 중독적 사용과 학습활동의 변화와 (2)상기 두 변수 간의 관계 방향성을 검증하고, (3)성별 간 다집단 복합 분석을 수행하였다. 이를 위하여 제 1~3차 한국아동 청소년패널 초4 데이터와 자기회귀교차지연모형 분석을 사용하였다. 분석 결과, 시간의 경과에 따라 초등학생의 휴대전화 중독적 사용과 학습활동은 지속적으로 정적 영향을 미치고, 이전 시점의 휴대전화 중독적 사용이 이후 시점의 학습활동에 부적으로 영향을 미치는 것으로 나타났다. 단, 학습활동은 휴대전화 중독적 사용에 종단적으로 유의한 영향을 미치지 않았고, 두 변수 간 종단적 관계 검정 결과에 대한 성별 간 차이는 유의하지 않았다. 본 연구는 연구결과를 바탕으로 초등학생의 휴대전화 중독적 사용 방지를 위한 지침 마련에 유용한 기초자료를 제공하였다.

• 응용통계연구
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• 제28권3호
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• pp.429-442
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• 2015

변동성(volatility)은 투자위험을 의미하며 자산의 가격결정이나 포트폴리오 관리 및 투자전략에서 아주 중요한 역할을 한다. 이러한 변동성을 모형화하기 위한 조건부 이분산 모형으로서 전통적인 GARCH(generalized autoregressive conditional heteroskedastic) 모형 및 확장된 형태들이 널리 사용되어지고 있으나, 금융위기와 재정위기와 같은 구조적 변화를 변동성 예측에 반영할 수 없다는 단점을 가지고 있다. 본 논문에서는 이를 극복하기 위한 모형으로서 국면전환 GARCH(Markov regime switching GARCH) 모형을 소개하고, 한국의 일별 KOSPI 수익률에 적용하여 변동성 분석 및 예측을 실시하고, 기존의 GARCH 모형들과 비교하여 그 성능을 평가한다. 그 결과 표본 내(in-sample)의 변동성 적합도 측면에서 국면전환 GARCH 모형이 가장 우수한 성능을 보였으며, 표본 외(out-of-sample) 예측력 측면에서는 국면전환 GARCH 모형이 단기적 예측에서 좋지 않은 성능을 보였으나 장기적 예측에서 우수함을 보였다.

• 대한간호학회지
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• 제49권1호
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• pp.69-79
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• 2019

Purpose: This study aimed to identify the directionality of the causal relationship and interaction between depression and amount of smoking over time in hardcore smokers using longitudinal descriptive analysis. Methods: Secondary data from the Korean Welfare Panel Study were analyzed using autoregressive cross-lagged modeling. Participants included 342 hardcore smokers who participated in the 8th to 11th waves of the panel study. Results: Analyses revealed that change(s) in depression levels according to time had a significant positive relationship with the total amount of smoking per day (${\beta}=.29$, ${\beta}=.19$, ${\beta}=.17$, p<.001), while change(s) in total amount of smoking per day according to time had a significant positive relationship with depression (${\beta}=.43$, ${\beta}=.50$, ${\beta}=.38$, p<.001). Analysis of the cross-lagged effect between depression and total amount of smoking per day showed that depression at one time point had a significantly positive relationship with the total amount of smoking per day at the next time point (${\beta}=.14$, ${\beta}=.13$, ${\beta}=.13$, p=.021), and that the total amount of smoking per day at one time point had a significant positive relationship with depression at the next time point (${\beta}=.04$, ${\beta}=.04$, ${\beta}=.03$, p=.044). Conclusion: The findings in the present study confirmed a cross-interaction between depression and total amount of smoking per day in hardcore smokers. The present findings could be used to develop appropriate smoking-related interventions.

• Journal of the Korean Statistical Society
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• 제35권4호
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• pp.409-417
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• 2006

This article is concerned with threshold modeling of the bifurcating autoregressive model (BAR) originally suggested by Cowan and Staudte (1986) for tree structured data of cell lineage study where each individual $(X_t)$ gives rise to two off-spring $(X_{2t},\;X_{2t+1})$ in the next generation. The triplet $(X_t,\;X_{2t},\;X_{2t+1})$ refers to mother-daughter relationship. In this paper we propose a threshold model incorporating the difference of 'fertility' of the mother for the first and second off-springs, and thereby extending BAR to threshold-BAR (TBAR, for short). We derive a sufficient condition of stationarity for the suggested TBAR model. Also various inferential methods such as least squares (LS), maximum likelihood (ML) and quasi-likelihood (QL) methods are discussed and relevant limiting distributions are obtained.

• Korean Journal of Hydrosciences
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• 제8권
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• pp.125-136
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• 1997

The simulation techniques of hydrologic data series have develped for the purposes of the design of water resources system, the optimization of reservoir operation, and the design of flood control of reservoir, etc. While the stochastic models are usually used in most analysis of water resources fields for the generation of data sequences, the indexed sequential modeling (ISM) method based on generation of a series of overlapping short-term flow sequences directly from the historical record has been used for the data generation in the western USA since the early of 1980s. It was reported that the reliable results by ISM were obtained in practical applications. In this study, we generate annual inflow series at a location of Hong Cheon Dam site by using ISM method and autoregressive, order-1 model (AR(1)), and estimate the drought characteristics for the comparison aim between ISM and AR(1).

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1100-1102
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• 2000

Most systems in tile real world are non-linear and can be represented by the non-linear autoregressive moving average (NARMA) model. The extension of fuzzy system for modeling the system that is represented by NARMA model will be proposed in this paper. Here, fuzzy basis function (FBF) is used as fuzzy NARMA(p,q) model. Then, an approach to Identify fuzzy NARMA models based on fuzzy basis functions is proposed. The efficacy of the proposed approach is shown from experimental results.