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- W2138568082 abstract "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." @default.
- W2138568082 created "2016-06-24" @default.
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- W2138568082 date "2006-12-01" @default.
- W2138568082 modified "2023-09-26" @default.
- W2138568082 title "A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection" @default.
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- W2138568082 doi "https://doi.org/10.1142/s021964920600158x" @default.
- W2138568082 hasPublicationYear "2006" @default.
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