Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN

Gülyeter Öztürk; Osman Eldoğan

doi:10.35377/saucis...1404116

EN

Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN

Abstract

Chaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data. Through successful prediction studies conducted on the generated chaotic time series, forecasts can be made about events displaying unpredictable behavior in nature, which have not yet been modeled. This enables preparation for both favorable and unfavorable situations that may arise. In this study, chaotic time series were generated using Lorenz, Chen, and Rikitake multivariate chaotic systems. To enhance prediction accuracy on the generated data, GRU, LSTM and RNN models were trained with different hyperparameters. Subsequently, comprehensive test studies were conducted to evaluate their performance. Predictions were calculated using evaluation metrics, including MSE, RMSE, MAE, MAPE, and R2. In the experimental study, each chaotic system was trained with different hyperparameter combinations on six network models. The experimental results indicate that the utilized models exhibited greater success in predicting chaotic time series compared to some other models in the literature.

Keywords

References

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Details

Primary Language

English

Subjects

Control Engineering, Mechatronics and Robotics (Other)

Journal Section

Research Article

Authors

Gülyeter Öztürk ^*
0000-0001-9157-3709
Türkiye

Osman Eldoğan
0000-0001-9236-8985
Türkiye

Early Pub Date

August 23, 2024

Publication Date

August 31, 2024

Submission Date

December 12, 2023

Acceptance Date

July 22, 2024

Published in Issue

Year 2024 Volume: 7 Number: 2

DOI

https://doi.org/10.35377/saucis...1404116

IZ

https://izlik.org/JA96FF66FJ

Cite

RIS / Bibtex

APA

Öztürk, G., & Eldoğan, O. (2024). Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN. Sakarya University Journal of Computer and Information Sciences, 7(2), 156-172. https://doi.org/10.35377/saucis...1404116

AMA

1.Öztürk G, Eldoğan O. Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN. SAUCIS. 2024;7(2):156-172. doi:10.35377/saucis.1404116

Chicago

Öztürk, Gülyeter, and Osman Eldoğan. 2024. “Prediction of Multivariate Chaotic Time Series Using GRU, LSTM and RNN”. Sakarya University Journal of Computer and Information Sciences 7 (2): 156-72. https://doi.org/10.35377/saucis. 1404116.

EndNote

Öztürk G, Eldoğan O (August 1, 2024) Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN. Sakarya University Journal of Computer and Information Sciences 7 2 156–172.

IEEE

[1]G. Öztürk and O. Eldoğan, “Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN”, SAUCIS, vol. 7, no. 2, pp. 156–172, Aug. 2024, doi: 10.35377/saucis...1404116.

ISNAD

Öztürk, Gülyeter - Eldoğan, Osman. “Prediction of Multivariate Chaotic Time Series Using GRU, LSTM and RNN”. Sakarya University Journal of Computer and Information Sciences 7/2 (August 1, 2024): 156-172. https://doi.org/10.35377/saucis. 1404116.

JAMA

1.Öztürk G, Eldoğan O. Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN. SAUCIS. 2024;7:156–172.

MLA

Öztürk, Gülyeter, and Osman Eldoğan. “Prediction of Multivariate Chaotic Time Series Using GRU, LSTM and RNN”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, Aug. 2024, pp. 156-72, doi:10.35377/saucis. 1404116.

Vancouver

1.Gülyeter Öztürk, Osman Eldoğan. Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN. SAUCIS. 2024 Aug. 1;7(2):156-72. doi:10.35377/saucis. 1404116

Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN

Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN

Abstract

Keywords

Öz

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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