| dc.creator | Barba-Maggi, Lida | |
| dc.creator | Rodríguez-Agurto, José Nibaldo | |
| dc.date | 2018-09-04T14:57:54Z | |
| dc.date | 2022-06-18T19:19:05Z | |
| dc.date | 2018-09-04T14:57:54Z | |
| dc.date | 2022-06-18T19:19:05Z | |
| dc.date | 2014-11-22 | |
| dc.date | 2014 | |
| dc.date | 2014-11-16 | |
| dc.date.accessioned | 2023-08-22T04:20:58Z | |
| dc.date.available | 2023-08-22T04:20:58Z | |
| dc.identifier | 1131105 | |
| dc.identifier | https://hdl.handle.net/10533/219750 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8319190 | |
| dc.description | Forecasting of time series implies time and effort in order to achieve
accuracy. In this work is proposed a new strategy of times series forecasting based
on the extraction components of low and of high frequency. The strategy imply
four stages, embedding, decomposition, estimation and recomposition. In the first
stage, is used the Hankel matrix to embed the original time series. In the second
stage is applied the Singular Value Decomposition (SVD) technique, with SVD
are extracted the components of low and high frequency. In the third stage is implemented
an Autoregressive Neural Network (ANN) based on Particle Swarm
Optimization (PSO), the ANN makes the estimation of the components. The recomposition
is the final stage and here is obtained the forecasted value, here is
computed with the single addition of the estimated components obtained in the
third stage. The evaluation of this proposal is developed with two time series of
traffic accidents occurred in Concepci´on Chile, from year 2000 to 2012, the data
sampling period is weekly. The results obtained are compared with the values
given by the conventional forecasting process, showing the high accuracy and superiority
of this proposal, the gain in MAPE is 498:3% and the gain in R2 is of
53:2% for the time series injured people, and with the time series injured people
was obtained a gain of similar proportion.
Keywords: Autoregressive Neural Network Particle Swarm Optimization Singular
Value Decomposition | |
| dc.language | eng | |
| dc.relation | instname: Conicyt | |
| dc.relation | reponame: Repositorio Digital RI2.0 | |
| dc.relation | Mexican International Conference on Artificial Intelligence (MICAI) | |
| dc.relation | info:eu-repo/grantAgreement//1131105 | |
| dc.relation | info:eu-repo/semantics/dataset/hdl.handle.net/10533/93486 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.title | Traffic accidents forecasting using Singular Value Decomposition and a neural network based on PSO | |
| dc.type | Ponencia | |
| dc.type | info:eu-repo/semantics/lecture | |
