Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents
Joint Authors
Barba, Lida
Montt, Cecilia
Rodríguez, Nibaldo
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented.
The strategy of forecasting is implemented using two stages.
In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD).
In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used.
The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm.
The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012.
The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%.
American Psychological Association (APA)
Barba, Lida& Rodríguez, Nibaldo& Montt, Cecilia. 2014. Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1048491
Modern Language Association (MLA)
Barba, Lida…[et al.]. Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1048491
American Medical Association (AMA)
Barba, Lida& Rodríguez, Nibaldo& Montt, Cecilia. Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1048491
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048491