• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.821-831
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• 2011

It has been relatively incomplete in the field of financial time series to adapt asymmetric features to multivar ate GARCH processes (McAleer et al., 2009). Retaining constant conditional correlation(CCC) structure, this article pursues to introduce asymmetric GARCH modelling in analysing multivariate volatilities in time series in a practical point of view. Multivariate Korean financial time series are analyzed in detail to compar our theory with conventional methodologies including GARCH and EGARCH.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.229-239
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• 2018

This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.843-853
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• 2014

Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.389-399
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• 2013

This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.

• 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.29 no.1
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• pp.27-39
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• 2016

This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsity comes from setting small coefficients to exact zeros. In the estimation perspective, Davis et al. (2015) showed that the lasso type of regularization method is successful because it provides a simultaneous variable selection and parameter estimation even for time series data. However, their simulations study reports that the regular lasso overestimates the number of non-zero coefficients, hence its finite sample performance needs improvements. In this article, we show that the adaptive lasso significantly improves the performance where the adaptive lasso finds the sparsity patterns superior to the regular lasso. Some tuning parameter selections in the adaptive lasso are also discussed from the simulations study.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.747-757
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• 2017

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.899-907
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• 2010

Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.211-211
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• 2016

도시 배수 시스템의 설계 및 홍수 방지 전략의 수립은 공통적으로 특정 재현 기간에 대한 극한강우량의 정보가 필요하다. 최근 이상기후로 인한 극한강우사상의 발생이 잦아짐에 따라, 수공 구조물의 설계 및 계획에 기후변화의 영향(특히 강수량)이 고려되어야 한다. 이에 본 연구에서는 설계에 사용되는 일반적인 재현기간의 IDF(intensity duration frequency)곡선에서 극한강우량을 산출하고, 이를 분석한 통계학적인 추세가 기후변화 시나리오의 IDF곡선의 작성에 미치는 영향에 대한 평가를 실시하였다. 연구 첫 단계에는 연 최대 일강수량 시계열의 추세를 분석하고 정량화하였다. 본 연구에서는 1970년부터 2015년까지의 60개 관측소의 연 최대 일강수량 시계열을 사용하여 분석하였으며, 관측소별로 다른 유의수준을 고려하여 Mann-Kendall test가 실시되었다. 그 결과 연구기간동안 증가 및 감소 추세가 발생하였다. 추세가 분석 및 정량화 되면, Gumbel 분포를 이용하여 극한강우량을 계산하였다. 마지막으로 지역별로 실시한 추세분석으로부터 얻은 정보를 통합하고 추세분석 결과를 바탕으로 2가지의 기후 시나리오를 규정하여 IDF곡선의 매개변수를 산정하였다. 그 결과, 통계학적인 추세의 증가 또는 감소 모두 각 지속시간의 극한강우시계열에 영향을 주는 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.405-421
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• 2019

Predicting how much energy to use is an important issue in society. However, it is more difficult to capture the usage characteristics of residential buildings than other buildings. This paper provides time series analysis methods for electricity consumption in a residential building. Temperature is closely related to electricity demand. An error correction model, which is a method of adjusting the error with time, is applied when a cointegration relation is established between variables. Therefore, we analyze data via ECMs with consideration of the temperature effect.