Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data

Authors

  • Muhammad Muharrom Al Haromainy UPN Veteran Jawa Timur
  • Dwi Arman Prasetya UPN Veteran Jawa Timur
  • Anggraini Puspita Sari UPN Veteran Jawa Timur

DOI:

https://doi.org/10.38043/tiers.v4i1.4326

Keywords:

Time Series Prediction, Recurrent Neural Networks (RNNs), Genetic Algorithms (GAs), Hyperparameter Optimization, Prediction Accuracy

Abstract

Stock price data or similar time series data can be used to carry out forecasting processes using past data. The method that can be used is like a neural network, one type of neural network that is used is the Recurrent Neural Network. When using the Recurrent Neural Network (RNN) method, we need to determine the appropriate parameters in order to get the best forecasting results. It takes experience or . In this study, this problem can be solved using optimization algorithms, such as Genetic Algorithms. With genetic algorithms, neural networks can be trained to get the best objective function. So that after implementing the RNN which was optimized using the Genetic Algorithm on stock time series data, when the trial was carried out without optimization the Genetic Algorithm got an RMSE value of 0.108, after being combined using the genetic algorithm it got an RMSE value of 0.106.

 

 

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References

R. C. and Y. Kan-ngan, “The Comparison of PM2.5 forecasting methods in the form of multivariate and univariate time series based on Support Vector Machine and Genetic Algorithm,” in International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2018.

R. L. S. A. and M. K. P. Akhila, “Climate Forecasting:Long short Term Memory Model using Global Temperature Data,” in International Conference on Computing Methodologies and Communication (ICCMC), 2022.

V. P. and F. M. D. Rao, D. Nandi, F. Pérez-Fontán, “Long Term Prediction of Rain Rate and Attenuation using ANN and RNN Algorithms,” in IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), 2021.

S. S. S. and M. A. A. R. Hatem M. Noaman, “Enhancing recurrent neural network-based language models by word tokenization,” Hum. Cent. Comput. Inf. Sci., vol. 8, no. 12, 2018.

J. L. Kyungmin Lee, Chiyoun Park, Namhoon Kim, “Accelerating recurrent neural network language model based online speech recognition system,” DMC R&D Center, Samsung Electron. Seoul, Korea, 2018.

Z. Zhang, H. Cheng, and T. Yang, “A recurrent neural network framework for flexible and adaptive decision making based on sequence learning,” Plos Comput. Biol., 2020.

Z. He, “Improving LSTM Based Acoustic Model with Dropout Method,” in International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), 2019.

X. C. and F. P. L. Depeng, X. Jianhua, “A modulation recognition method based on enhanced data representation and convolutional neural network,” 2021 IEEE 4th Adv. Inf. Manag. Commun. Electron. Autom. Control Conf., 2021.

X. T. and Z. Z. F. Li, X. Yu, “Short-Term Load Forecasting for an Industrial Park Using LSTM-RNN Considering Energy Storage,” in Asia Energy and Electrical Engineering Symposium (AEEES), 2021.

A. A. and B. Kanisha, “Forecasting stock market price using LSTM-RNN,” in International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022.

Y. Y. and G. Wang, “Self-Adaptive Genetic Algorithm For Bucket Wheel Reclaimer Real-Parameter Optimization,” IEEE Access, vol. 7, 2019.

G. Papazoglou, “Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem,” Multidiscip. Digit. Publ. Inst., 2023.

C. Lamini, S. Benhlima, and A. Elbekri, “Genetic algorithm based approach for autonomous mobile robot path planning,” Procedia Comput. Sci., vol. 127, pp. 180–189, 2018.

S.-U. P. and S.-K. H. J. -H. Han, D. -J. Choi, “Hyperparameter Optimization for Multi-Layer Data Input Using Genetic Algorithm,” in International Conference on Industrial Engineering and Applications (ICIEA), 2020.

X. Gong, “Optimized layout methods based on optimization algorithms for DPOS,” Aerosp. Sci. Technol., 2019.

A. T. and G. Ünal, “A RNN based time series approach for forecasting turkish electricity load,” Signal Process. Commun. Appl. Conf., vol. 26, 2018.

M. M. H. and M. M. K. S. B. Islam, “Prediction of Stock Market Using Recurrent Neural Network,” in Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021.

M. S. H. and H. Mahmood, “Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast,” IEEE Access, 2020.

R. V. R. and K. Subramaniam, “Optimized Wavelet Filter from Genetic Algorithm, for Image Compression,” in International Conference on Smart Structures and Systems (ICSSS), 2020.

H. Chung and K. Shin, “Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction,” MDPI, 2018.

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Published

2023-06-25

How to Cite

1.
Al Haromainy MM, Prasetya DA, Sari AP. Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data. TIERS [Internet]. 2023Jun.25 [cited 2024May18];4(1):16-24. Available from: https://journal.undiknas.ac.id/index.php/tiers/article/view/4326

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