• 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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• 제13권2호
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• pp.265-274
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• 2000

대기오염 자료와 사망자수 자료를 이용하여 대기중 총분진이 사망자수에 미치는 영향을 1996~1997년 서울 자료를 이용하여 분석하였다. 대기오염 자료에서 보이는 강한 계절성과 장기간의 경향 그리고 기온과 습도의 비선형관계를 고려하여 일반화 부가모형(generalized additive model)을 사용하였고, 그 결과 대기중 총분진 100$\mu$g/㎥ 증가시 초과사망자는 3%(95% 신뢰구간 : 0.8-5.2)로 추정되었다. 사례-교차연구(case-crossover study)의 결과를 보면 한 방향(후향적, 전향적)으로의 대조기간 설정 방법은 편향된 추정치를 주었고 양방향 방법은 회귀계수의 추정치는 일반화 부가모형과 거의 같은 결과를 주었지만 표준오차는 상대적으로 큰 값을 주었다. 이상에서 볼 때 대기오염 자료의 분석과 같이 계절성이 큰 자료에서는 사례-교차 연구를 적용할 때 주의가 요구됨을 알 수 있었다.

• Asian Journal of Business Environment
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• 제7권4호
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• pp.35-41
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• 2017

Purpose - This paper aims to examine several time series models to predict sales of department stores and discount store markets in South Korea, while other previous trial has performed sales of convenience stores and supermarkets. In addition, optimal predicted values on the underlying model can be got and be applied to distribution industry. Research design, data, and methodology - Two retailing types, under investigation, are homogeneous and comparable in size based on 86 realizations sampled from January 2010 to February in 2017. To accomplish the purpose of this research, both ARIMA model and exponential smoothing methods are, simultaneously, utilized. Furthermore, model-fit measures may be exploited as important tools of the optimal model-building. Results - By applying Holt-Winters' additive seasonality method to sales of two large-scale retailing types, persisting increasing trend and fluctuation around the constant level with seasonal pattern, respectively, will be predicted from May in 2017 to February in 2018. Conclusions - Considering 2017-2018 forecasts for sales of two large-scale retailing types, it is important to predict future sales magnitude and to produce the useful information for reforming financial conditions and related policies, so that the impacts of any marketing or management scheme can be compared against the do-nothing scenario.