An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm-An Application for Aerosol Particle Number Concentrations

Ola M. Surakhi, Martha Arbayani Zaidan, Sami Serhan, Imad Salah, Tareq Hussein

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Article number89
Issue number4
Number of pages26
Publication statusPublished - Dec 2020
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences
  • 112 Statistics and probability

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