International Journal of Computational Intelligence Systems

Volume 12, Issue 2, 2019, Pages 1144 - 1161

Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization

Authors
Thieu Nguyen1, Tu Nguyen1, Binh Minh Nguyen1, *, Giang Nguyen2
1 School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
2 Institute of Informatics, Slovak Academy of Sciences, Dubravska cesta 9, Bratislava, Slovakia
*Corresponding author. Email: minhnb@soict.hust.edu.vn
Corresponding Author
Binh Minh Nguyen
Received 14 April 2019, Accepted 26 September 2019, Available Online 4 November 2019.
DOI
https://doi.org/10.2991/ijcis.d.190930.003How to use a DOI?
Keywords
Meta-heuristics, Coral reefs optimization, Opposition-based learning, Neural networks, Time series forecasting, Nature-inspired algorithms, Distributed systems
Abstract

In this paper, a novel algorithm called opposition-based coral reefs optimization (OCRO) is introduced. The algorithm is built as an improvement for coral reefs optimization (CRO) using opposition-based learning (OBL). For efficient modeling as the main part of this work, a novel time series forecasting model called OCRO-multi-layer neural network (MLNN) is proposed to explore hidden relationships in the non-linear time series data. The model thus combines OCRO with MLNN for data processing, which enables reducing the model complexity by faster convergence than the traditional back-propagation algorithm. For validation of the proposed model, three real-world datasets are used, including Internet traffic collected from a private internet service provider (ISP) with distributed centers in 11 European cities, WorldCup 98 contains request numbers to the server in football world cup season in 1998, and Google cluster log dataset gathered from its data center. Through the carried out experiments, we demonstrated that with both univariate and multivariate data, the proposed prediction model gains good performance in accuracy, run time and model stability aspects as compared with other modern learning techniques like recurrent neural network (RNN) and long short-term memory (LSTM). In addition, with used real datasets, we intend to concentrate on applying OCRO-MLNN to distributed systems in order to enable the proactive resource allocation capability for e-infrastructures (e.g. clouds services, Internet of Things systems, or blockchain networks).

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1144 - 1161
Publication Date
2019/11
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.190930.003How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Thieu Nguyen
AU  - Tu Nguyen
AU  - Binh Minh Nguyen
AU  - Giang Nguyen
PY  - 2019
DA  - 2019/11
TI  - Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 1144
EP  - 1161
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190930.003
DO  - https://doi.org/10.2991/ijcis.d.190930.003
ID  - Nguyen2019
ER  -