• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2009년도 춘계학술대회 논문집
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• pp.97-100
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• 2009

육상의 지표면 파라미터는 기후와 주로 연관되어 있으므로 육상 관측 위성 영상에 나타나는 많은 물리적 과정은 계절 주기에 따른 시간적 변화를 보인다. 본 연구에서는 계절에 따라 변하는 물리적 과정을 포함하는 시계일 원격 탐사 영상 시리즈를 어댑티브 피드백 시스템에 의해 복원한다. 이 시스템에서는 계절적 변화를 추적하기 위하여 하모닉 모델을 사용하고 수치 영상 모형의 공간적 의존성을 나타내기 위해 깁슨 랜덤 필드를 사용한다. 복원과정을 통하여 구성된 하모닉 모델과 어댑티브 계수에 의해 지표면 연속적 변화를 감시할 수 있다. 본 연구에서는 1996년부터 2000년까지 한반도로부터 관측된 AVHRR 영상 시리즈를 일주일 간격으로 정적 합성하여 NOVI 시리즈를 구하고 하모닉 모델을 사용하는 어댑티브 복원 시스템을 이 NDVI 시리즈를 적용하여 한반도 지표면 변화를 추적하였다. 연구 결과는 하모닉 어댑티브 복원시스템이 거의 실시간으로 지표면 변화를 감시하는데 매우 효과적인 수단이 될 것이라는 잠재성을 보여준다.

• Journal of the Korean Statistical Society
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• 제9권1호
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• pp.79-94
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• 1980

This paper illustrates the application of the Box-Jenkins techniques for analyzing the monthly data of money supply from January 196 to December 1977 of Korea.

• 유통과학연구
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• 제14권11호
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• 응용통계연구
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• 제34권3호
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• pp.439-447
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• 2021

범지구적 이상기후의 잦은 출현으로 기상 변화에 대한 관련 연구가 활발히 진행되고 있지만, 장기간 축적된 기상자료를 이용한 경향성 분석 연구는 부족하였다. 본 연구에서는 비모수적 분석방법을 이용해 40년간 종관기상관측장비(ASOS)로 부터 축적된 기온 시계열 자료의 경향성을 분석하였다. 남한지역의 연평균 기온과 계절별 평균기온 시계열 자료에 대한 Mann-Kendall 검정 결과 상승 경향성이 존재하는 것으로 나타났다. 또한 Pettitt 검정을 적용해 탐색된 변동점을 전후로 경향성의 정도를 파악할 수 있는 Sen's slope를 계산한 결과, 변동점 이후의 최근 자료에서 기온의 상승 경향성이 더욱 큰 것을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.473-481
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• 2010

본 연구는 최근에 활발히 연구가 진행 중인 항공수요 예측을 위하여 계절형 다변량 시계열 모형을 기반으로 하고 다른 모형과의 비교를 RMSE(Root Mean Square Error)를 기준으로 비교한 것이다. 여기서 싱가폴 국제항공유가, 수출액을 추가하여 예측성능을 좋게 하고자 한다.

• 융합경영연구
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• 제9권2호
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• pp.1-10
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• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.

• 응용통계연구
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• 제24권1호
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• pp.93-102
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• 2011

본 연구는 관광수요 예측 분야에서 사용되는 계절형 ARIMA 모형과 다변량 계절형 시계열 모형과 오차수정모형의 성능을 비교한 것이다. 본 연구에서는 일본, 중국, 미국, 필리핀에 대한 실제 자료를 이용한 결과 관광 수요에는 계절성이 중요한 역할을 하는 것을 보이고 각 국가별로 예측 정확도를 RMSE를 기준으로 하여 비교하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• 응용통계연구
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• 제28권6호
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• pp.1235-1243
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• 2015

최근 들어 ICT 분야의 발달에 따라 데이터 사용량의 급격한 증가로 인터넷 트래픽 사용량 예측은 중요성은 강조되고 있다. 이러한 예측치를 적절한 트래픽 관리와 제어를 위한 계획 수립에 도움을 준다. 본 논문은, 5분 단위의 인터넷 트래픽 자료를 이용하여 결합 예측 모형을 제안하고자 한다. 이에 대하여 시계열의 대표적인 3개 모형인 Seasonal ARIMA, Fractional ARIMA(FARIMA), Taylor의 수정된 Holt-Winters 모형을 적용하였다. 모형 간 결합 예측 방법으로 예측치 간의 SA(Simple Average) 결합 예측 방법과 OLS(Ordinary Least Square)를 이용한 결합방법, ERLS(Equality Restricted Least Squares)를 이용한 결합 예측 방법, Armstrong(2001)이 제안한 MSE 기반 결합 예측 방법을 사용한다. 이에 따른 결과로서 3시간에서의 예측은 Seasonal ARIMA가 선택된 반면, 6시간 이후 예측에서는 결합 예측 방법이 좋은 예측 성능을 보여준다.

• 패션비즈니스
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• 제15권6호
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• pp.176-203
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• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.