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  • 标题:An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations
  • 本地全文:下载
  • 作者:Ola M. Surakhi ; Martha Arbayani Zaidan ; Sami Serhan
  • 期刊名称:Computers
  • 电子版ISSN:2073-431X
  • 出版年度:2020
  • 卷号:9
  • 期号:4
  • 页码:89-114
  • DOI:10.3390/computers9040089
  • 出版社:MDPI Publishing
  • 摘要:Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions.
  • 关键词:ensemble learning; heuristic algorithm; optimization; recurrent neural network ensemble learning ; heuristic algorithm ; optimization ; recurrent neural network
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