English, Article edition: A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection José Guajardo; Richard Weber; Jaime Miranda

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Bookmark: http://trove.nla.gov.au/version/133489
Physical Description
  • article
Language
  • English

Edition details

Title
  • A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection
Author
  • José Guajardo
  • Richard Weber
  • Jaime Miranda
Physical Description
  • article
Notes
  • Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we propose such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the regression model optimizing its parameters dynamically. We develop a particular technique for feature selection as well as for model construction. The methodology, however, is a generic one providing the opportunity to employ alternative approaches within our framework. The application to several time series underlines its usefulness.
  • Support vector regression, time series forecasting, feature selection
  • RePEc:wsi:jikmxx:v:05:y:2006:i:04:p:329-335
Language
  • English
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