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An optimal stacked ensemble deep learning model for predicting time-series data using a genetic algorithm

An application for aerosol particle number concentrations
 
: Surakhi, Ola M.; Zaidan, Martha Arbayani; Serhan, Sami; Salah, Imad; Hussein, Tareq

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Volltext ()

Computers 9 (2020), Nr.4, Art. 89, 26 S.
ISSN: 2073-431X
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer WKI ()
ensemble learning; heuristic algorithm; optimization; recurrent neural network

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-621397.html