• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.121-121
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• 2008

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.1-17
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• 2008

In this paper the cargo transportation were forecasted for the North Port in Busan through time series models. The cargo transportation were classified into three large groups; container, oil, general cargo. The seasonal indexes of existing cargo transportation were firstly calculated, and optimum models were chosen among exponential smoothing models and ARIMA models. The monthly cargo transportation were forecasted with applying the seasonal index in annual cargo transportation expected from the models. Thus, the cargo transportation in 2011 and 2015 were forecasted about 22,900 myriad ton and 24,654 myriad ton respectively. It was estimated that container cargo volume would play the role of locomotive in the increase of the future cargo transportation. On the other hand, the oil and general cargo have little influence upon it.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.401-409
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• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.331-338
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• 2012

Irregularity data inspected by EM-120, an railway inspection system in Korea includes unavoidable incomplete and erratic information, so it is encountered lots of problem to analyse those data without appropriate pre-data-refining processes. In this research, for the efficient management and maintenance of railway system, characteristics and problems of the detected track irregularity data have been analyzed and efficient processing techniques were developed to solve the problems. The correlation between track irregularity and seasonal changes was conducted based on ARIMA model analysis. Finally, time series analysis was carried out by various forecasting model, such as regression, exponential smoothing and ARIMA model, to determine the appropriate optimal models for forecasting track irregularity progress.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1573-1583
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• 2016

It is necessary to check mutual correlations between related variables because housing prices are influenced by a lot of variables of the economy both internally and externally. In this paper, employing the Granger causality test, we have validated interrelated relationship between the variables. In addition, there is cointegration associations in the results of the cointegration test between the variables. Therefore, an analysis using a vector error correction model including an error correction term has been attempted. As a result of the empirical comparative analysis of the forecasting performance with ARIMA and VAR models, it is confirmed that the forecasting performance by vector error correction model is superior to those of the former two models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1153-1159
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• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.997-1021
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• 2010

This paper compares the two most widely used seasonal adjustment methods: the X-12-ARIMA and TRAMO-SEATS procedures. The basic features of these methods are discussed and compared in both their theoretical and empirical aspects. In doing so, the X-13A-S program is used to reevaluate their applicability to Korean macroeconomic data by considering possible structural breaks in the series. The finding is that both methods provide very reliable and stable estimates of seasonal factors and seasonally adjusted data. As for the empirical comparisons, TRAMO-SEATS appears to outperform X-12-ARIMA, although the results are somewhat mixed depending on the comparison criteria used and on the series under analysis. In particular, the performance of TRAMO-SEATS turns out to compare more favorably when seasonal adjustment is carried out to each sub-samples (by taking possible structural breaks into account) than when the whole sample period is used. The result suggests that as the model-based TRAMO-SEATS has a considerable theoretical appeal, some features of TRAMO-SEATS should further be incorporated into X-12-ARIMA until a standard and integrated procedure is reached by combining the theoretical coherence of TRAMO-SEATS and the empirical usefulness of X-12-ARIMA.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of Distribution Science
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• v.14 no.11
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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.