Sliding window-based support vector regression for predicting micrometeorological data
作者：
Highlights：
• A new methodology for predicting micrometeorological data is proposed.
• Our proposed method involves a novel combination of SVR and ensemble learning.
• Weak learners built from efficient extracted data is aggregated dynamically.
• Large-scale micrometeorological data to compare other methods is used.
• The best prediction performance and the lowest time complexity are achieved.
摘要
•A new methodology for predicting micrometeorological data is proposed.•Our proposed method involves a novel combination of SVR and ensemble learning.•Weak learners built from efficient extracted data is aggregated dynamically.•Large-scale micrometeorological data to compare other methods is used.•The best prediction performance and the lowest time complexity are achieved.
论文关键词：Predicting micrometeorological data,Data extraction,Dynamic aggregation,Support vector regression,Ensemble learning
论文评审过程：Received 4 February 2016, Revised 29 March 2016, Accepted 13 April 2016, Available online 23 April 2016, Version of Record 6 May 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.04.012
