Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents.
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 smoothe...
| Publicado en: | Scientific World Journal pp. 152375 - 152376 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109756130&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109756130 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109756130 NLM25243200 2012741497 10.1155/2014/152375 NLM25243200 PMC4163352 109756130 ppf: 152375 ppct: 1 formats: tig: atl: Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents. aug: au: Barba, Lida Rodríguez, Nibaldo Montt, Cecilia affil: Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile ; Universidad Nacional de Chimborazo, 33730880 Riobamba, Ecuador. sug: subj: Accidents, Traffic Trends Interrupted Time Series Analysis Trends Neural Networks (Computer) Forecasting Human Interrupted Time Series Analysis Methods ab: 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%. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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