Year: 2020
Author: Chen Yu, Haochen Li, Jiangjiang Xia, Hanqiuzi Wen, Pingwen Zhang
Communications in Computational Physics, Vol. 28 (2020), Iss. 4 : pp. 1305–1320
Abstract
In this paper, the RSEL (Random Subfeature Ensemble Learning) algorithm is 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 the features and avoid the influence of a small number of unstable outliers generated randomly. Furthermore, the feature engineering schemes are designed for the weather forecast data to make full use of spatial or temporal context. RSEL algorithm is tested by forecasting the wind speed and direction, and it improves the forecast accuracy of traditional methods and has good robustness.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2020-0006
Communications in Computational Physics, Vol. 28 (2020), Iss. 4 : pp. 1305–1320
Published online: 2020-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 16
Keywords: Weather forecasting ensemble learning machine learning feature engineering.
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