• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.719-723
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• 2004

본 연구에서는 일 최고, 최소치 유출량 계열을 확충하기 위해 ARIMA(p,d,q) 모형을 이용하였으며, 분석 자료의 경향성 유무를 파악하기 위해 Mann-Kendal 비모수적 검정을 실시하였다. 분석 결과, 최고 최소 유출량 자료 모두 경향성이 없는 것으로 분석되었다. ARIMA(p,d,q) 모형의 최적 차수를 결정하기 위해 ACF, PACF, AIC, 그리고 SBC(Schwarz Bayesian Criterion) 검사를 실시하였으며 이를 통해 최적의 ARMA 모형을 결정하였다. 일 최대치 자료의 경우 추계학적 경향 보다는 무작위적 특성을 보였으며, 일 최소치 자료계열 경우, ARMA(1,0) 모형이 최적 모형으로 선정되었다.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.47-56
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• 1997

The asymptotic distribution of the extended Yule-Walker estimates of a mixed ARMA process is derived. Also, a recursive algorithm is derived to calculate the extended Yule-Walker estimates recursively, which is a generalization of the Levinson-Durbin algorithm.

• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.47-58
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• 1992

The autocorrelation function structures of bilinear time series model BL(p, q, r, s), $r \geq s$ are obtained and shown to be analogous to those of ARMA(p, l), l=max(q, s). Simulation studies are performed to investigate the adequacy of Akaike information criteria for identification between ARMA(p, l) and BL(p, q, r, s) models and for determination of orders of BL(p, q, r, s) models. It is suggested that the model of having minimum Akaike information criteria is selected for a suitable model.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1661-1671
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• 2016

Risk analysis is a systematic study of uncertainties and risks we encounter in business, engineering, public policy, and many other areas. Value at Risk (VaR) is one of the most widely used risk measurements in risk management. In this paper, the Korean Composite Stock Price Index data has been utilized to model the VaR employing the classical ARMA (1,1)-GARCH (1,1) models with normal, t, generalized hyperbolic, and generalized pareto distributed errors. The aim of this paper is to compare the performance of each model in estimating the VaR. The performance of models were compared in terms of the number of VaR violations and Kupiec exceedance test. The GARCH-GPD likelihood ratio unconditional test statistic has been found to have the smallest value among the models.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.87-92
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• 2008

It is common to use CUSUM charts for detecting small level shifts in processes control, in which reference value(k) and decision interval(h) are the design parameters to be determined. To control process with autocorrelation, CUSUM charts could be applied to residuals obtained from fitting ARIMA models. However, constant level shifts in processes lead to varying mean shifts in residual processes and thus standard CUSUM charts may need to be modified. In this paper, we study the performance of CUSUM charts with various design parameters applied to autocorrelated processes, especially focussing on ARMA(1,1) models, and propose how they can be determined to get better performance in terms of the average run length.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.317-326
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• 1998

Water consumption characteristics on the northern part of Seoul were analyzed using wavelet transform with a base function of Coiflets 5. It turns out that long term evolution mode detected at 212 scale in 1995 was in a shape of hyperbolic tangent over the entire period due to the development of Sanggae resident site. Furthermore, there was seasonal water demand having something to do with economic cycle which reached its peak at the ends of June and December. The amount of this additional consumption was about $1,700\;\textrm{cm}^3/hr$ on June and $500\;\textrm{cm}^3/hr$ on December. It was also shown that the periods of energy containing sinusoidal component were 3.13 day, 33.33 hr, 23.98 hr and 12 hr, respectively, and the amplitude of 23.98 hr component was the most humongous. The components of relatively short frequency detected at $2^i$[i = 1,2,…12] scale were following Gaussian PDF. The most reliable predictive models are multiple AR[32,16,23] and ARMA[20, 16, 10, 23] which the input of temperature from the view point of minimized predictive error, mutual independence or residuals and the availableness of reliable meteorological data. The predicted values of water supply were quite consistent with the measured data which cast a possibility of the deployment of the predictive model developed in this study for the optimal management of water supply facilities.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.395-400
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• 2006

Among the various error sources in positioning and navigation, the paper focuses on the modeling and prediction of receiver clock bias and then tries to achieve positioning based on simulated and predicted clock bias. With the SA off, it is possible to model receiver clock bias more accurately. We selected several types of GNSS receivers for test using ARMA model. To facilitate prediction with short and limited sample pseudorange observations, AR and ARMA are compared, and the improved AR model is presented to model and predict receiver clock bias based on previous solutions. Our work extends to clock bias prediction and positioning based on predicted clock bias using only 3 satellites that is usually the case under urban canyon situation. In contrast to previous experiences, we find that a receiver clock bias can be well modeled using adopted ARMA model. Test has been done on various types of GNSS receivers to show the validation of developed model. To further develop this work, we compare solution conditions in terms of DOP values when point positioning is conducted using 3 satellites to simulate urban positioning environment. When condition allows, height component is derived from other ways and can be set as known values. Given this condition, location is possible using less than 2 GNSS satellites with fixed height. Solution condition is also discussed for this background using mode of constrained positioning. We finally suggest an effective predictive time span based on our test exploration under varied conditions.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1257-1268
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• 2014

Since the introduction of KTX (Korea Tranin eXpress) in Korea reilway market, number of passengers using KTX has been greatly increased in the market. Thus, demand forecasting for KTX passengers has been played a importantant role in the train operation and management. In this paper, we study several time series models and compare the models based on considering special days and others. We used the MAPE (Mean Absolute Percentage Errors) to compare the performance between the models and we showed that the Reg-AR-GARCH model outperformanced other models in short-term period such as one month. In the longer periods, the Reg-ARMA model showed best forecasting accuracy compared with other models.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.73-82
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• 2020

Most process date such as semiconductor and petrochemical processes, autocorrelation often exists between observed data, but when the existing SPC(Statistical process control) is applied to these processes, it is not possible to effectively detect the average change of the process. In this paper, when the average change of a certain size occurs in the process data following a specific time series model, the average of the residuals changes according to the passage of time, and the change pattern of the average is introduced around the ARMA(1,1) process. Based on this result, the reference value required in the design process of the CUSUM (Cumulative sum) control chart is appropriately considered by considering the type of the time series model of the process data of the CUSUM control chart that can detect small mean changes in the process and the width of the process mean change of interest. It was confirmed through simulation that it should be selected and used.