• 경영과학
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• 제20권1호
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• 응용통계연구
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• 제28권5호
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• pp.885-893
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• 2015

통계청과 한국은행 등 통계작성기관에서 이용되고 있는 계절조정은 연간 경제통계 작성시 시계열을 예측한 후 계절조정방법을 적용하여 1년 후 계절변동을 예측하고 원통계 작성시 원통계에서 이를 제거하여 계절조정계열을 작성하고 있다. 이 경우 계절변동을 효과적으로 예측하는 것이 계절조정계열의 품질 향상을 위해 무엇보다 중요하다. 계절변동은 1년 단위로 비슷한 함수적 형태를 지니면서 변하므로 계절변동은 일종의 함수적 시계열이다. 함수적 시계열은 함수적 주성분분석을 바탕으로 한 함수적 시계열모형으로 예측할 수 있다. 본 연구에서는 함수적 시계열 모형을 이용하여 향후 1년간 계절변동을 예측하는 방안을 마련하고 X-11 방식 등 기존의 예측방법과 비교하여 유용성을 파악하였다.

• 응용통계연구
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• 제29권1호
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• pp.205-220
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• 2016

경제통계에서 기조적 변동인 추세변동과 순환변동을 살펴보려면 경제통계에서 달력변동을 포함한 계절변동을 적절히 제거하는 계절조정이 필요하다. 계절조정방법으로는 전년동기대비 증감률과 같이 간편한 방식이 있지만 통계작성기관에서는 이동평균 또는 시계열모형을 기반으로 한 X-12-ARIMA 또는 TRAMO-SEATS를 이용하여 계절조정계열을 작성한다. 통계청과 한국은행은 X-12-ARIMA 또는 X-13ARIMA-SEATS에 우리나라 고유의 명절, 공휴일 등을 추가로 보정한 계절조정방법을 만들고 이를 이용하여 우리나라 주요 경제통계의 계절조정계열을 작성, 공표하고 있다. 본 논문에서는 그 동안의 연구를 바탕으로 계절조정의 기본 원리와 우리나라의 계절조정 현황을 정리하고, 월별 산업생산지수(제조업)와 취업자의 계절조정을 통해 계절조정의 주요 쟁점을 정리하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제3권1호
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• Advances in environmental research
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• 제6권1호
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• pp.53-65
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• 2017

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 15 physico-chemical parameters collected from 7 monitoring stations in a river during the years from 2014 to 2016 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except alkalinity, which is always the most important parameters in contributing to water quality variations for all three seasons.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.101-115
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• 2005

We propose in this article a procedure for testing unit and fractional orders of integration, with the roots simultaneously occurring in the trend, the seasonal and the cyclical component of the time series. The tests have standard null and local limit distributions. However, finite sample critical values are computed, and several Monte Carlo experiments conducted across the paper show that the rejection frequencies against unit (and fractional) orders of integration are relatively high in all cases. The tests are applied to the UK consumption and income series, the results showing the importance of the roots corresponding to the trend and the seasonal components and, though the unit roots are found to be fairly suitable models, we show that fractional processes (including one for the cyclical component) may also be plausible alternatives in some cases.

• 대한전기학회논문지:전력기술부문A
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• 제50권6호
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• pp.279-286
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• 2001

A load modeling, micro method, is performed by component load modeling, load composition rate estimation and aggregation of component load model, etc. The load model obtained from this process must be applied to actual load bus to verify it and to get reliable load model. But it is difficult to apply every load bus due to al lot of load buses and complex experiment. This paper proposed the field test method in load bus to verify the load modeling. For appropriate field test, representative load buses are selected by the proposed algorithm considering the composition rate of user category in all load buses. The field tests were performed at selected load buses to obtain load characteristics of bus by time and seasonal without blackout. The results of measurement and analysis are presented in detail.

• 한국환경보건학회지
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• 제26권2호
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• pp.41-48
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• 2000

This study has been carried out to determine the seasonal characteristics of concentration of various ionic (CI-, NO3-, SO42-, Na+, NH+, K+, Ca2+) and heavy metallic (Pb, Mn, Cu, Ni) species in Pusan from August 1997 to April 1998. The concentrations of CI-, Na+, K+ were higher during summer with 2.98 ${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of but the concentration of NH4+ was higher during winter with 2.46${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of heavy metals(Pb, Cu, Mn, Ni) were 186.0 ng/㎥ in summer, 222.6 ng/㎥ in autumn, and 135.83 ng/㎥ in winter. Over the seasons inspected, the concentration of Mn was higher in coarse particles than fine particles and concentration of Ni was higher in fine particles than coarse particles. during yellow sand period, the concentration of TSP was increased about two times than that of other period. SO42-, Ca2+ concentrations were higher than other ionic components because of soil particles. The concentration of Ni showed 94.62ng/㎥ was increased about 4~5 times than other period. Principal component of the yellow sand, SO42-, Ca2+ could be discreased by rainfall and washout effect of atmospheric aerosol was higher in coarse particles than fine particles. Results from PCA(principal component analysis) showed that major pollutant was NaCl by seasalt particulate and (NH4)2SO4.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.141-147
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• 2006

BOK-X12-ARIMA has been developed by the Bank of Korea in order to accomodate special features such as lunar effect, labor day and election effect which are intrinsic in Korean seasonal time series. Irregular component resulting from BOK-X12-ARIMA is usually treated as white noise time series. If this shows dependency structure, it may be advisable to incorporate dependency in irregular component into prediction. This article illustrates how to refine point forecast using dependency structure in irregular component.

• 수산해양기술연구
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• 제46권2호
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• pp.126-138
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• 2010

To investigate seasonal variation of fisheries resources composition and their correlationships with environmental factors in the coastal ecosystem of the middle Yellow Sea of Korea, shrimp beam trawl were carried out for the fisheries survey. Fisheries resources of 81 species, 57 families, and 6 taxa totally were collected by shrimp beam trawl in the middle coastal ecosystem of Yellow Sea of Korea. Species were included 6 species in Bivalvia, 6 in Cephalopoda, 22 in Crustacea, 2 in Echinodermata, 5 in Gastropoda, and 40 in Pisces. Diversity indices (Shannon index, H') showed seasonal variation with low value of 2.14 in winter, and high value of 2.67 in spring. Main dominant species were Oratosquilla oratoria, Octopus ocellatus, Acanthogobius lactipes, Cynoglossus joyneri, Rapana venosa venosa, Loligo beka, Chaeturichthys stigmatias, Raja kenojei, Microstomus achne and Paralichthys olivaceus, that were occupied over 58% of total individuals, and 55% of wet weight. Fisheries organism made four coordinative seasonal groups by the principal component analysis (PCA), showing stronger seasonal variation than spatial variation. PC from PCA showed statistically significant cross-correlationships with seawater temperature, $NH_4$-N, TP and chlorophyll a (P < 0.05).