한국해양대학교

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컨테이너 물동량을 이용한 인공신경망과 ARIMA모형의 예측력 비교에 관한 연구

Title
컨테이너 물동량을 이용한 인공신경망과 ARIMA모형의 예측력 비교에 관한 연구
Alternative Title
A Comparative Study on the Container Port Throughput Forecasting using Neural Network and ARIMA Models
Author(s)
이지원
Publication Year
2008
Publisher
한국해양대학교 대학원
URI
http://kmou.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002176000
http://repository.kmou.ac.kr/handle/2014.oak/10337
Abstract
The forecast of a container traffic has been very important for terminal plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. Time series data has trend and seasonality. In this paper, a ANNs methodology that make a consideration of trend and seasonal. The result with terminal traffic data indicate that effectiveness can differ according to the characteristics of terminals.
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동북아물류시스템학과 > Thesis
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000002176000.pdf Download

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