A study on the ability of support vector regression and neural networks to forecast basic time series patterns

Recently, novel learning algorithms such as Support Vector Regression (SVR) and Neural Networks (NN) have received increasing attention in forecasting and time series prediction, offering attractive theoretical properties and successful applications in several real world problem domains. Commonly, t...

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Detalles Bibliográficos
Autores principales: Crone, Sven F., Weber, Richard, Guajardo, José
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
learning algorithms
comparison
Radial Basis Functions (RBF)
Neural nets
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23879
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23879
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
learning algorithms
comparison
Radial Basis Functions (RBF)
Neural nets
Algorithms
spellingShingle Ciencias Informáticas
learning algorithms
comparison
Radial Basis Functions (RBF)
Neural nets
Algorithms
Crone, Sven F.
Weber, Richard
Guajardo, José
A study on the ability of support vector regression and neural networks to forecast basic time series patterns
topic_facet Ciencias Informáticas
learning algorithms
comparison
Radial Basis Functions (RBF)
Neural nets
Algorithms
description Recently, novel learning algorithms such as Support Vector Regression (SVR) and Neural Networks (NN) have received increasing attention in forecasting and time series prediction, offering attractive theoretical properties and successful applications in several real world problem domains. Commonly, time series are composed of the combination of regular and irregular patterns such as trends and cycles, seasonal variations, level shifts, outliers or pulses and structural breaks, among others. Conventional parametric statistical methods are capable of forecasting a particular combination of patterns through ex ante selection of an adequate model form and specific data preprocessing. Thus, the capability of semi-parametric methods from computational intelligence to predict basic time series patterns without model selection and preprocessing is of particular relevance in evaluating their contribution to forecasting. This paper proposes an empirical comparison between NN and SVR models using radial basis function (RBF) and linear kernel functions, by analyzing their predictive power on five artificial time series: stationary, additive seasonality, linear trend, linear trend with additive seasonality, and linear trend with multiplicative seasonality. Results obtained show that RBF SVR models have problems in extrapolating trends, while NN and linear SVR models without data preprocessing provide robust accuracy across all patterns and clearly outperform the commonly used RBF SVR on trended time series.
format Objeto de conferencia
Objeto de conferencia
author Crone, Sven F.
Weber, Richard
Guajardo, José
author_facet Crone, Sven F.
Weber, Richard
Guajardo, José
author_sort Crone, Sven F.
title A study on the ability of support vector regression and neural networks to forecast basic time series patterns
title_short A study on the ability of support vector regression and neural networks to forecast basic time series patterns
title_full A study on the ability of support vector regression and neural networks to forecast basic time series patterns
title_fullStr A study on the ability of support vector regression and neural networks to forecast basic time series patterns
title_full_unstemmed A study on the ability of support vector regression and neural networks to forecast basic time series patterns
title_sort study on the ability of support vector regression and neural networks to forecast basic time series patterns
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23879
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