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Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training
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Seung-Won Shin (Sangmyung University)
Jong-Uk Choi (Sangmyung University)
Jeong-Hyun Rho (Hanyang University)
Vol. 4, No. 2, Page: 23 ~ 34
Abstract
Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.
Show/Hide Detailed Information in Korean
Wavelet 변환과 신경망을 이용한 시계열 데이터 예측력의 향상
신승원 (상명대학교 AIT연구소 연구원)
최종욱 (상명대학교 정보통신학부)
노정현 (한양대학교 도시공학과)
Keywords
웨이브렛 변환, RBF 신경망, 통행시간 예측
Cite this article
JIIS Style
Shin, S.-W., J.-U. Choi, and J.-H. Rho, "Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training ", Journal of Intelligence and Information Systems, Vol. 4, No. 2 (1998), 23~34.

IEEE Style
Seung-Won Shin, Jong-Uk Choi, and Jeong-Hyun Rho, "Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training ", Journal of Intelligence and Information Systems, vol. 4, no. 2, pp. 23~34, 1998.

ACM Style
Shin, S.-W., Choi, J.-U., and Rho, J.-H., 1998. Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training . Journal of Intelligence and Information Systems. 4, 2, 23--34.
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@article{Shin:JIIS:1998:60,
author = {Shin, Seung-Won and Choi, Jong-Uk and Rho, Jeong-Hyun},
title = {Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training },
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 1998},
volume = {4},
number = {2},
month = Dec,
year = {1998},
issn = {2288-4866},
pages = {23--34},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
}
%0 Journal Article
%1 60
%A Seung-Won Shin
%A Jong-Uk Choi
%A Jeong-Hyun Rho
%T Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 4
%N 2
%P 23-34
%D 1998
%R
%I Korea Intelligent Information System Society