• 한국환경과학회지
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• 제7권4호
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• pp.433-440
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• 1998

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIU models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking strate. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water Quality by ARU model with the observed one. The models are ARIMA(2,0,0)$\times$(0,1,1)l2 for total nitrogen, ARIMA(0,1,1)x(0,1,1)l2 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.

• 한국습지학회지
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• 제1권1호
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• pp.17-28
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• 1999

The purpose of this study was to analysis water quality monitoring data and to estimate future trends using ARIMA model of time series analysis. Water quality data in Chin yang reservoir were used with monthly monitoring interval during past 7 years. The variations of water quality parameters with periodicity and trend could be estimated by multiplicative ARIMA models and the statistical tests showed a good agreement with the observed data. Therefore, the monthly values of water quality parameters could be forecasted using these models.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1994년도 추계학술발표회 발표논문집; 연세대학교경영관, 서울; 8 Oct. 1994
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• pp.99-99
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• 1994

• 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.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• 한국가스학회지
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• 제19권2호
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• pp.20-28
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• 2015

화재를 예방하기 위해서는 그에 필요한 정책과 평가가 필요하며, 화재 발생에 대한 적절한 모니터링 기법이 있어야 한다. 이를 위해 품질관리의 기법중의 하나인 관리도를 화재 모니터링에 적용하는 연구가 진행된 바 있다. 그 결과, 통계적으로 겨울에는 많은 화재가 발생한다는 것이 증명되었고, 매년마다 어떤 주기를 띠고 변화한다는 것이 발견되었다. 그럼에도 불구하고, 여름철과 겨울철에 동일한 기준(관리한계선)을 적용할 경우 상대적으로 발생건수가 과다한 겨울에는 과도한 예방활동이 진행되고, 여름에는 잠재이상이 있는 경우에도 발생건수 감소에 따른 착시현상으로 인해 활동이 부족해질 가능성이 있다. 이때, 각 계절별로 다른 관리 한계선을 적용시킨다면 합리적인 예측과 보다 효과적인 이상 패턴의 모니터링이 가능해질 수 있다. 따라서 본 연구에서는 발생건수를 대상으로 시간에 따라 변하는 시계열 모델을 사용하여 화재발생 빈도 예측 모델계수의 체계적인 설정과정을 ARIMA 모델을 기반으로 제안하였다. 이를 바탕으로 화재발생 패턴의 개선된 분석과 이에 기반한 보다 체계적인 예방활동을 진행할 수 있을 것으로 기대한다.

• 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.

• 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.

• 무역학회지
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• 제45권5호
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• pp.75-89
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• 2020

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.121-121
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• 2008