• 응용통계연구
• /
• 제29권1호
• /
• pp.205-220
• /
• 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.

• 경영과학
• /
• 제20권1호
• /
• pp.103-124
• /
• 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.

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

• 응용통계연구
• /
• 제36권5호
• /
• pp.457-471
• /
• 2023

월별 시계열은 일별 시계열의 월별 합이지만, 일별 시계열을 대체로 관측할 수 없어서 요일구성변동, 명절·공휴일변동 등 달력변동을 가상적으로 가정한 가변수를 포함한 RegARIMIA 모형을 이용하여 추정하고 있다. 일별 시계열을 관측할 수 있다면 요일구성변동, 명절·공휴일변동 등 달력변동을 일별 시계열을 바탕으로 추정할 수 있고 이를 이용하여 월별 시계열의 계절조정을 개선할 수 있다. 이 논문에서는 일별 시계열의 달력변동 추정을 이용하여 월별 시계열의 계절조정을 개선하는 방법을 제안하고, 이 방법을 적용하여 3개의 월별 시계열을 계절조정하고 기존의 X-13ARIMA-SEATS를 이용한 계절조정과 비교하였다.

• Fisheries and Aquatic Sciences
• /
• 제4권3호
• /
• pp.101-111
• /
• 2001

In an attempt to demonstrate the seasonal variation of the Ulleung Warm Eddy (UWE), in which the UWE changes its shape from a warm core ring in early spring to a warm lens in late summer under the effect of surrounding East Korean Warm Current (EKWC) Water, a simple geostrophic adjustment model is considered. Model results indicate that the buoyancy increase of the EKWC Water and the strengthening of the EKWC towards summer, both of which are typical of this region, are the major factors governing the seasonal variation of the UWE.

• Communications for Statistical Applications and Methods
• /
• 제7권2호
• /
• pp.513-523
• /
• 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
• /
• 제13권2호
• /
• pp.225-236
• /
• 2000

계절변동조정에 대한 전문적인 지식이 없는 통계작성(이용)자가 계절변동조정통계를 정교히 작성하기 위해서는 계절변동조정작업을 전산화하여 체계화할 필요가 있다. 본고에서는 X-12-ARIMA에 바탕은 둔 BOK-X-12-ARIMA프로그램의 개발 내역을 설명하고 향후 개선방향을 정리하였다.

• 응용통계연구
• /
• 제23권2호
• /
• pp.275-284
• /
• 2010

본 연구에서는 계절조정을 위한 사전조정 단계 중 명절효과 조정방법에 이용되는 파급유형을 소개하고, 기존의 파급유형보다 다양하고 유연한 형태를 갖는 새로운 파급유형을 제안하였다. 그리고 명절 전후의 시계열 파급형태가 같지 않다는 현실적인 가정 하에 기존의 파급유형과 새로 제안한 파급유형을 비교하였다. 비교연구에서는 기존의 것과 새로 제안된 것으로 가능한 모든 파급유형을 구성한 후 RegARIMA로 효과를 추정하였으며 추정과정에는 우리나라의 산업별 생산지수와 출하지수 자료를 사용하였다.

• Journal of the korean society of oceanography
• /
• 제37권3호
• /
• pp.134-143
• /
• 2002

Using OGCM results, we have shown that the ring-like cold baroclinic eddies associated with cyclonic circulation are shed from late summer to early fall near the Izu-Ogasawara Ridge from the Kuroshio Extension owing to baroclinic instability. On the other hand, warm baroclinic eddies are generated by the intensified western boundary current associated with the warm anomaly accumulated near the Ridge in winter, which corresponds to the basin-wide barotropic intensification of the wind-driven gyre in winter. We are successful in reproducing the behavior of those meso-scale eddies using a simple two-layer primitive equation model driven by seasonal winds associated with the positive curl. Those eddies carry barotropic seasonal signals originated in the Pacific Basin quite slowly west of the ridge; this process introduces a phase lag in the timing of the seasonal maximum transport in the Philippine Basin west of the ridge. It Is demonstrated that the existence of bottom topography, baroclinicity, and nonlinearity due to advection are three necessary elements for the generation of these eddies south of Japan.

• 한국태양에너지학회:학술대회논문집
• /
• 한국태양에너지학회 2009년도 추계학술발표대회 논문집
• /
• pp.26-31
• /
• 2009

This study was analysed influence of urban higher temperature in Busan about time series analysis of AWS data. The results are as follows. (1) The temperature of Busan show min $13.2^{\circ}C$ ~max $15.8^{\circ}C$ by 50 years, it is on the rise. (2) The seasonal adjustment series, summer appeared min $17.5^{\circ}C$ ~max $28.9^{\circ}C$ with primitive series similarly. The winter was min $-11.4^{\circ}C$ ~max $17.9^{\circ}C$, the minimum temperature was more lowly than primitive series and maximum temperature was more higher than primitive series. The results, seasonal adjustment series is guessed with influence difference urban structural element beside seasonal factor. (3) Regional analytical result, January appeared with range of min 28% ~max 196% of the seasonal factor and August appeared min 90% ~ max 106%. One of the case which is of 100% or more of the seasonal factor January 12nd~17th, August appears at the 15~17th.