Physical time series prediction using dynamic neural network inspired by the immune algorithm

Abir Jaafar Hussain; Haya Al-Askar; Dhiya Al-Jumeily

doi:10.1007/978-3-319-11298-5_16

Physical time series prediction using dynamic neural network inspired by the immune algorithm

Abir Jaafar Hussain
, Haya Al-Askar
, Dhiya Al-Jumeily

Liverpool John Moores University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Time series analysis is a fundamental subject that has been addressed widely in different fields. It has been exploited and used in different scientific fields for example, natural, biomedical, economic and industrial data as well as financial time series. In this paper, we consider the application of a novel neural network architecture inspired by the immune algorithm and the recurrent links for the prediction of Lorenz and earthquake time series by exploiting the inherent temporal capabilities of the recurrent neural model. The performance of this network is benchmarked against "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron network, a Jordan and an Elman neural network as well as the self organized neural network inspired by the immune algorithm. The results indicate that the inherent temporal characteristics of the recurrent links network make it extremely well suited to the processing of time series based data.

Original language	English
Title of host publication	Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings
Publisher	Springer Verlag
Pages	152-161
Number of pages	10
ISBN (Print)	9783319112978
DOIs	https://doi.org/10.1007/978-3-319-11298-5_16
State	Published - 2014
Externally published	Yes
Event	3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014 - Bournemouth, United Kingdom Duration: 8 Sep 2014 → 10 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8779 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014
Country/Territory	United Kingdom
City	Bournemouth
Period	8/09/14 → 10/09/14

Keywords

and physical time series prediction
Recurrent neural network
self organised neural network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-11298-5_16

Cite this

Hussain, A. J., Al-Askar, H., & Al-Jumeily, D. (2014). Physical time series prediction using dynamic neural network inspired by the immune algorithm. In Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings (pp. 152-161). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8779 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-11298-5_16

Hussain, Abir Jaafar ; Al-Askar, Haya ; Al-Jumeily, Dhiya. / Physical time series prediction using dynamic neural network inspired by the immune algorithm. Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings. Springer Verlag, 2014. pp. 152-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Hussain, AJ, Al-Askar, H & Al-Jumeily, D 2014, Physical time series prediction using dynamic neural network inspired by the immune algorithm. in Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8779 LNAI, Springer Verlag, pp. 152-161, 3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014, Bournemouth, United Kingdom, 8/09/14. https://doi.org/10.1007/978-3-319-11298-5_16

Physical time series prediction using dynamic neural network inspired by the immune algorithm. / Hussain, Abir Jaafar; Al-Askar, Haya; Al-Jumeily, Dhiya.
Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings. Springer Verlag, 2014. p. 152-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8779 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Al-Askar, Haya

AU - Al-Jumeily, Dhiya

PY - 2014

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N2 - Time series analysis is a fundamental subject that has been addressed widely in different fields. It has been exploited and used in different scientific fields for example, natural, biomedical, economic and industrial data as well as financial time series. In this paper, we consider the application of a novel neural network architecture inspired by the immune algorithm and the recurrent links for the prediction of Lorenz and earthquake time series by exploiting the inherent temporal capabilities of the recurrent neural model. The performance of this network is benchmarked against "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron network, a Jordan and an Elman neural network as well as the self organized neural network inspired by the immune algorithm. The results indicate that the inherent temporal characteristics of the recurrent links network make it extremely well suited to the processing of time series based data.

AB - Time series analysis is a fundamental subject that has been addressed widely in different fields. It has been exploited and used in different scientific fields for example, natural, biomedical, economic and industrial data as well as financial time series. In this paper, we consider the application of a novel neural network architecture inspired by the immune algorithm and the recurrent links for the prediction of Lorenz and earthquake time series by exploiting the inherent temporal capabilities of the recurrent neural model. The performance of this network is benchmarked against "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron network, a Jordan and an Elman neural network as well as the self organized neural network inspired by the immune algorithm. The results indicate that the inherent temporal characteristics of the recurrent links network make it extremely well suited to the processing of time series based data.

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Hussain AJ, Al-Askar H, Al-Jumeily D. Physical time series prediction using dynamic neural network inspired by the immune algorithm. In Adaptive and Intelligent Systems - Third International Conference, ICAIS 2014, Proceedings. Springer Verlag. 2014. p. 152-161. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11298-5_16

Physical time series prediction using dynamic neural network inspired by the immune algorithm

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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