The SVM Approach for Box-Jenkins Models

Saeid Amiri; Dietrich von Rosen; Silvelyn Zwanzig

The SVM Approach for Box-Jenkins Models

Saeid Amiri
, Dietrich von Rosen
, Silvelyn Zwanzig

Institutionen för energi och teknik

Publikation: Bidrag till tidskrift › Artikel i vetenskaplig tidskrift › Peer review

Sammanfattning

Support Vector Machine (SVM) is known in classification and regression modeling. It has been receiving attention in the application of nonlinear functions. The aim is to motivate the use of the SVM approach to analyze the time series models. This is an effort to assess the performance of SVM in comparison with ARMA model. The applicability of this approach for a unit root situation is also considered.

Originalspråk	Engelska
Sidor (från-till)	23-36
Antal sidor	14
Tidskrift	REVSTAT-Statistical Journal
Volym	7
Nummer	1
Status	Publicerad - 2009

Nyckelord

Support Vector Machine
time series analysis
unit root

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title = "The SVM Approach for Box-Jenkins Models",

abstract = "Support Vector Machine (SVM) is known in classification and regression modeling. It has been receiving attention in the application of nonlinear functions. The aim is to motivate the use of the SVM approach to analyze the time series models. This is an effort to assess the performance of SVM in comparison with ARMA model. The applicability of this approach for a unit root situation is also considered.",

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AU - von Rosen, Dietrich

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AB - Support Vector Machine (SVM) is known in classification and regression modeling. It has been receiving attention in the application of nonlinear functions. The aim is to motivate the use of the SVM approach to analyze the time series models. This is an effort to assess the performance of SVM in comparison with ARMA model. The applicability of this approach for a unit root situation is also considered.

KW - Support Vector Machine

KW - time series analysis

KW - unit root

KW - Support Vector Machine

KW - time series analysis

KW - unit root

UR - https://res.slu.se/id/publ/48812

M3 - Journal article

SN - 1645-6726

VL - 7

SP - 23

EP - 36

JO - REVSTAT-Statistical Journal

JF - REVSTAT-Statistical Journal

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