A Study of Combination Forecasting Optimized by Genetic Algorithm Weights
This paper proposes a method for optimizing combination forecasting weights using genetic algorithms,aiming to en-hance the accuracy of combination forecasting models by selecting optimal weights through the optimization process of genetic algo-rithms.Firstly,a grid search method is employed to explore suitable genetic algorithm parameters within the hyperparameter space.Subsequently,through operations such as natural selection,crossover,and mutation in genetic algorithms,optimal weights applica-ble to combination forecasting are gradually evolved.Finally,empirical analysis of the national railway operational mileage demon-strates that the weights determined by genetic algorithms exhibit superior forecasting performance in combination forecasting,show-casing higher accuracy and generalization capability compared to traditional methods.
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