• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.513-523
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• 2000

The X-12-ARIMA program was utilized on the analysis of the time series trend on 76 Korean industrial activities data in order to ensure that the trading day effect adjustment as well as the seasonal effect adjustment is needed to extract the fundamental trend-cycle factors from various economic time series data. The trading day effect is strongly correlated with the activity of production and shipping but not with the activity of inventory. Furthermore, the industrial activities were classified with respect to the sensitivity on the tranding day effect.

• 응용통계연구
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• 제23권6호
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• pp.997-1021
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• 2010

본 연구에서는 최근에 새롭게 개발된 X-13A-S 프로그램을 이용하여 우리나라 경제시계열에 적합한 계절조정방법을 모색하였다. 특히 한국의 주요 거시경제지표에 대하여 X-12 필터와 SEATS 방법을 각각 적용하여 계절조정계열을 산출하고, 안정성 멱등성 등 계절조정의 적합성 평가기준을 토대로 두 방법의 유용성에 대한 이론적 실증적 비교분석을 시도하였다. 본 연구의 분석에 의하면 두 방법 모두 안정성이 우수한 것으로 나타나 계절조정의 신뢰성은 높은 것으로 평가되었다. 두 방법 사이의 상대적 비교에서는 대상 자료에 따라 약간 다른 결과를 보이고 있기는 하지만 전체적으로 모형에 기초하여 계절조정을 시행하는 SEATS 방법이 우월한 것으로 나타났다. 특히 구조변화를 고려하여 구간을 나누어 계절변동조정을 시행하면, 전체 기간에 대한 분석에 비해 SEATS 방법에 상대적으로 더 유리한 결과가 도출되었다. 이는 모형 분석에 기초를 둔 TRAMO-SEATS 방법이 계절조정의 이론적 정합성 및 일관성 측면에서 더 우수하다는 최근의 학문적인 연구 결과와 일치한다. 이러한 결과는 그 동안 현재 국내에서 사용되고 있는 X-12-ARIMA 방법 이외에 TRAMO-SEATS 방법을 한국 경제시계열 자료에 적용할 필요성이 있다는 것을 암시하는 것이라 할 수 있다. 따라서 향후 X-13A-S 프로그램과 같이 두 방법을 같이 병행하면서 이를 우리 자료에 맞게 조정하는 방안에 관한 지속적인 연구가 필요하다. 이를 통해 계량모형에 기초한 TRAMO-SEATS 방법의 이론적 정합성과 X-12-ARIMA 방법의 실증적 적합성을 결합시킬 수 있는 새로운 차원의 계절조정방법을 계속 모색해야 한다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• 한국농공학회지
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• 제18권2호
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• pp.4116-4120
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• 1976

With the use of many rivers increased nearly to the capacity, the need for information concerning daily quantities of water and the total annual or seasonal runoff has became increased. A systematic record of the flow of a river is commonly made in terms of the mean daily discharge Since. a single observation of stage is converted into discharge by means of rating curve, it is essential that the stage discharge relations shall be accurately established. All rating curves have the looping effect due chiefly to channel storage and variation in surface slope. Loop rating curves are most characteristic on streams with somewhat flatter gradients and more constricted channels. The great majority of gauge readings are taken by unskilled observers once a day without any indication of whether the stage is rising or falling. Therefore, normal rating curves shall show one discharge for one gauge height, regardless of falling or rising stage. The above reasons call for the correction of the discharge measurements taken on either side of flood waves to the theoretical steady-state condition. The correction of the discharge measurement is to consider channel storage and variation in surface slope. (1) Channel storage As the surface elevation of a river rises, water is temporarily stored in the river channel. There fore, the actual discharge at the control section can be attained by substracting the rate of change of storage from the measured discharge. (2) Variation in surface slope From the Manning equation, the steady state discharge Q in a channel of given roughness and cross-section, is given as {{{{Q PROPTO SQRT { 1} }}}} When the slope is not equal, the actual discharge will be {{{{ { Q}_{r CDOT f } PROPTO SQRT { 1 +- TRIANGLE I} CDOT TRIANGLE I }}}} may be expressed in the form of {{{{ TRIANGLE I= { dh/dt} over {c } }}}} and the celerity is approximately equal to 1.3 times the mean watrr velocity. Therefore, The steady-state discharge can be estimated from the following equation. {{{{Q= { { Q}_{r CDOT f } } over { SQRT { (1 +- { A CDOT dh/dt} over {1.3 { Q}_{r CDOT f }I } )} } }}}} If a sufficient number of observations are available, an alternative procedure can be applied. A rating curve may be drawn as a median line through the uncorrected values. The values of {{{{ { 1} over {cI } }}}} can be yielded from the measured quantities of Qr$.$f and dh/dt by use of Eq. (7) and (8). From the 1/cI v. stage relationship, new vlues of 1/cI are obtained and inserted in Eq. (7) and (8) to yield the steady-state discharge Q. The new values of Q are then plotted against stage as the corrected steadystate curve.