In the article the tournament searching method is used for optimization of the forecasting model based on the Nadaraya-Watson estimator. This is a nonparametric regression model useful for forecasting the nonstationary in mean and variance time series with multiple seasonal cycles and trend. The tournament searching is a stochastic global optimization algorithm which is easy to use and competitive to other stochastic methods such as evolutionary algorithms. Three types of tournament searching algorithms are proposed: for estimation of the forecasting model parameters (continuous optimization), for the predictor selection (binary optimization) and for both predictor selection and parameter estimation (mixed binary-continuous optimization). The effectiveness of the proposed approach is illustrated through applications to electrical load forecasting and compared with other optimization methods: grid search method, genetic and evolutionary algorithms, and sequential methods of feature selection. Application examples confirm good properties of tournament searching. © 2014 Springer International Publishing.
CITATION STYLE
Dudek, G. (2014). Tournament searching method for optimization of the forecasting model based on the Nadaraya-Watson estimator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8468 LNAI, pp. 339–348). Springer Verlag. https://doi.org/10.1007/978-3-319-07176-3_30
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