TY  - JOUR
PY  - 2014//
TI  - Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents
JO  - ScientificWorldJournal
A1  - Barba, Lida
A1  - Rodríguez, Nibaldo
A1  - Montt, Cecilia
SP  - 152375
EP  - 152375
VL  - 2014
IS  - 
N2  - 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%.<p />  <p>Language: en</p>
LA  - en
SN  - 2356-6140
UR  - http://dx.doi.org/10.1155/2014/152375
ID  - ref1
ER  -