Time Series Forecasting with Volume Weighted Support Vector Machines

Żbikowski, Kamil

doi:10.1007/978-3-319-06932-6_24

Cited by 3 publications

(1 citation statement)

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“…SVMs come under the category of non-parametric classifiers, and have many types of kernels. The kernel functions are introduced in SVMs to solve classification or regression difficulties in which the data are not linearly separable [ 17 ]. The various types of SVMs with kernels are shown in Table 1 , where γ, d , and r are kernel parameters.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

El-Sayed

Sankar

Daraghmi

et al. 2018

Sensors

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Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

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Section: Theoretical Backgroundmentioning

confidence: 99%