• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.139-155
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• 2011

This study is to estimate the dynamic change of the regional railway passenger traffic and, based on the estimated, to forecast the future regional railway passenger traffic by using the Seasonal ARIMA model. The existing studies using ARIMA failed to consider seasonality nor the monthly or the quarterly data. It was attempted in this study to use the monthly regional railway passenger traffic data to propose a model that estimates dynamic change of demand. The authors employed the Seasonal ARIMA model previously developed and used (1) the numbers of monthly passenger data and (2) the monthly passenger-km data. The test results showed that the numbers of passengers in 2015 and 2020 would increase by 36% and 71%, respectively, compared to those in 2008. The numbers of passenger-kms in 2015 and 2020 would increase by 25% and 78%, respectively, compared to those in 2008.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.149-162
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• 2007

The purpose of this study is to forecast the marine trading volumes using multiplicative seasonal Autoregressive Integrated Moving Average(ARIMA) model. The paper proceeds by comparing the forecasting performances of the unload volumes with those of the load volumes with Box-Jenkins ARIMA model. Also, I present the predicted values based on the ARIMA model. The result shows that the trading volumes increase very slowly.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.394-394
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• 2022

최근 스마트 수도미터의 보급을 통해 수용가구별 물 사용 자료를 수집할 수 있다. 이런 수용가구별 물 사용 패턴은 주말, 날씨 등 다양한 요인으로 인해 비선형적 특성을 가지고 있다. 그로인해 전통적인 시계열 예측 모형인 ARIMA 모형으로 적용하기 어렵다. 따라서 본 연구에서는 딥러닝 기반의 LSTM 모형을 통해 수용가구별 물 소비량 예측 모형을 개발하였다. 이 모형은 비선형적인 물 소비 패턴을 학습하기 위해 다양한 변수를 고려하였다. 서로 다른 종류의 4개 type (A : 단독주택, B: 아파트, C: 음식점, D : 초등학교)의 수용가구에 대한 ARIMA 모형과 LSTM 모형을 개발하였고, 학습에 사용되지 않은 새로운 데이터를 적용하여 정량적으로 예측성능을 비교했다. 그 결과, 모든 수용가구에서 LSTM 모형이 ARIMA 모형보다 성능이 우수하였다 (상관계수 : 평균89% | RMSE : 평균 5.60m³). 따라서 본 연구에서 제안한 모형은 수용가구별 물 사용량을 예측하는데 높은 활용도를 보일 것으로 기대된다.

• Convergence Security Journal
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• v.8 no.2
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• pp.35-40
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• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

• Journal of agriculture & life science
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• v.45 no.5
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• pp.115-126
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• 2011

This paper estimates the number of Korean cattle using time-series ARIMA model. This study classifies the structure of the number of cattle into six indexes to reflect the characteristics of cattle. This study apply ARIMA model to these six indexes according to Box-Jenkins procedure to identify, estimate and predict. The rates of slaughter for aged female and aged male cow is analyzed as non-stationary time series which has unit roots and other 4 indexes is analyzed as stationary time series. The differencing is applied to get rid of non-stationarity for the non-stationary time series. The results show that the number of cattle will be reduced from 2012 as a higher point and rebounded from 2018 as a lower point.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.124-130
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• 2011

This study was conducted to find out appropriate model and forecast visit demand of korea national parks using seasonal ARIMA model. Data of monthly visitors uses of 18 korea national parks from January, 2003 to December, 2010 was used to analyze. The result showed that $ARIMA(1,0,0)(1,1,0)_{12}$ model was selected as a appropriate model to forecast visit demand of korea national parks and the result of post evaluation used by index of mean absolute percentage error was accurate. Therefore, the result of this study will enhance reliability and validity of forecasting technique and contribute to management strategy of korea national park.

• Journal of the Korean Society for Railway
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• v.17 no.4
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• pp.307-312
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• 2014

This study suggested the ARIMA model taking into consideration the seasonal characteristic factor as a method for efficiently forecasting passenger transport demand of the Joongang Line. The forecasting model was built including the demand for the central inland region tourist train (O-train, V-train), which was opened to traffic in April-, 2013 and run in order to reflect the recent demand for the tourism industry. By using the monthly time series data (103) from January-, 2005 to July-, 2013, the optimum model was selected. The forecasting results of passenger transport demand of the Joongang Line showed continuous increase. The developed model forecasts the short-term demand of the Joongang Line.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.10a
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• pp.108-109
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• 2010

본 연구에서는 비선형예측기법으로서 그 우수성을 인정받고 있는 인공신경망모형을 사용하여 컨테이너 물동량 예측을 수행하였다. 그러나 인공신경망모형을 사용해 시계열의 예측결과를 ARIMA모형과 같이 널리 알려진 다른 전통적인 수요예측기법들과 비교 평가한 과거 연구들을 보게 되면 각기 주장하는 바와 그 결론이 상반됨을 알 수 있다. 그래서 인공신경망의 예측성과를 높이기 위한 기존의 선행연구들의 다양한 시도들을 바탕으로 국내 항만의 컨테이너물동량을 예측하고, 그를 통해 여러 모형간의 비교 검증작업을 수행하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.133-135
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• 2013

장래의 해상교통량에 대한 정확한 예측은 항로설계 및 해상교통의 안전성 평가 측면에서 중요한 요소이다. 본 연구는 신뢰성 있는 해상교통량을 추정하기 위해 시계열 모델의 지수평활법과 ARIMA 모형을 이용하여 모형의 식별 및 진단 방안을 제시하였다. 제시된 방법의 효과를 검증하기 위하여 주요항만인 부산항, 광양항, 인천항, 평택항의 해상교통량을 예측하였다. 그 결과로 부산항은 ARIMA 모형, 광양항은 Winters 승법 모형, 인천항은 단순계절 모형, 평택항은 ARIMA 모형이 더 적합한 모형으로 알 수 있었으며, 각 항만별 계절에 따라 월별 교통량의 차이를 보이는 것으로 분석되었다. 본 연구 결과는 향후 항로 및 항만설계 또는 해상교통 안전성 평가에 보다 신뢰성 있는 추정치를 제공할 수 있을 것으로 보인다.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.3-18
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• 2013

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.