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A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency.
The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix.
The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models.
Three time series coming from traffic accidents domain are used.
They represent the number of persons with injuries in traffic accidents of Santiago, Chile.
The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12.
The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT).
SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated.
The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.
American Psychological Association (APA)
Barba, Lida& Rodríguez, Nibaldo. 2017. A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1141111
Modern Language Association (MLA)
Barba, Lida& Rodríguez, Nibaldo. A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1141111
American Medical Association (AMA)
Barba, Lida& Rodríguez, Nibaldo. A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1141111
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141111