Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization

Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward as well as recurrent neural networks through training with PSO. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are used to train feedforward ANN (FANN) and Elman ANN (EANN) models. A novel nonlinear hybrid architecture is proposed to incorporate the training strengths of all these three PSO algorithms. Experiments are conducted on four real-world time series with the three forecasting models, viz. Box-Jenkins, FANN and EANN. Obtained results clearly demonstrate the superior forecasting performances of all three PSO algorithms over their BP counterpart for both FANN as well as EANN models. Both PSO and BP based neural networks also achieved notably better accuracies than the statistical Box-Jenkins methods. The forecasting performances of the neural network models are further improved through the proposed hybrid PSO framework.

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Genetic algorithm and Particle Swarm Optimization of ensemble neural networks with type-1 and type-2 fuzzy integration for prediction of the Taiwan Stock Exchange

2016 IEEE 8th International Conference on Intelligent Systems (IS) ◽

10.1109/is.2016.7737413 ◽

2016 ◽

Cited By ~ 3

Author(s):

Martha Pulido ◽

Patricia Melin

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Particle Swarm Optimization ◽

Stock Exchange ◽

Particle Swarm ◽

Swarm Optimization ◽

Taiwan Stock Exchange ◽

Ensemble Neural Networks

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Particle Swarm Optimization of Ensemble Neural Networks with Type-1 and Type-2 Fuzzy Integration for the Taiwan Stock Exchange

Nature-Inspired Design of Hybrid Intelligent Systems - Studies in Computational Intelligence ◽

10.1007/978-3-319-47054-2_27 ◽

2016 ◽

pp. 409-421 ◽

Cited By ~ 1

Author(s):

Martha Pulido ◽

Patricia Melin ◽

Olivia Mendoza

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Stock Exchange ◽

Particle Swarm ◽

Swarm Optimization ◽

Taiwan Stock Exchange ◽

Ensemble Neural Networks

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Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

Química Nova ◽

10.1590/s0100-40422013000600007 ◽

2013 ◽

Vol 36 (6) ◽

pp. 783-789 ◽

Cited By ~ 10

Author(s):

Francisco S. de Albuquerque Filho ◽

Francisco Madeiro ◽

Sérgio M. M. Fernandes ◽

Paulo S. G. de Mattos Neto ◽

Tiago A. E. Ferreira

Keyword(s):

Neural Networks ◽

Time Series ◽

Particle Swarm Optimization ◽

Artificial Neural Networks ◽

Particle Swarm ◽

Time Series Forecasting ◽

Pollutant Concentration ◽

Swarm Optimization ◽

Artificial Neural ◽

Concentration Levels

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Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

2020 12th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci49185.2020.9177737 ◽

2020 ◽

Author(s):

Xin Li ◽

Weijie Ren ◽

Jingying Zhao ◽

Min Han

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Network Model ◽

Neural Network Model ◽

Particle Swarm ◽

Time Series Forecasting ◽

Chaotic Time Series ◽

Swarm Optimization

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Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽

10.3390/en14041213 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1213

Author(s):

Ahmed Aljanad ◽

Nadia M. L. Tan ◽

Vassilios G. Agelidis ◽

Hussain Shareef

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Solar Irradiance ◽

Particle Swarm ◽

Time Interval ◽

Time Intervals ◽

Swarm Optimization ◽

Backpropagation Neural Networks ◽

Proposed Model ◽

Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

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