Tournament Searching Method for Optimization of the Forecasting Model Based on the Nadaraya-Watson Estimator

Abstract

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.