A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic

Shin, Chang-Hoon; Jeong, Su-Hyun

Journal of Navigation and Port Research 2011;35(1):83-91.
Published online February 28, 2011.

항만물동량 예측력 제고를 위한 ARIMA 및 인공신경망모형들의 비교 연구

신창훈, 정수현

A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic

Chang-Hoon Shin, Su-Hyun Jeong

Abstract

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

Key Words: ARIMA모형; Container traffic;forecasting;ARIMA model;ANN model;Hybrid model;MLP;TDNN;GA