A Data-Driven Random Subfeature Ensemble Learning Algorithm for Weather Forecasting (Communications in Computational Physics, 2020)
In this paper, the RSEL (Random Subfeature Ensemble Learning) algorithmis proposed to improve the forecast results of weather forecasting. Based on the classical machine learning algorithms, RSEL algorithm integrates random subfeature selection and ensemble learning combination strategy to enhance the diversity of thefeatures and avoid the influence of a small number of unstable outliers generated randomly. Furthermore, the feature engineering schemes are designed for the weatherforecast data to make full use of spatial or temporal context. RSEL algorithm is testedby forecasting the wind speed and direction, and it improves the forecast accuracy oftraditional methods and has good robustness.
Keywords: Weather forecasting; ensemble learning; machine learning; feature engineering.