A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

Lin, Rui-Chang

doi:10.1155/2016/3542898

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…Some self-similar and linear models were thus used to model and predict network traffic data. Nowadays, with the rapid development of network scale, network traffic proved to be a typical nonlinear time series with the characteristics of time-varying, long correlation, self-similarity, sudden, chaotic and so on [3,4] . Given that, the traditional linear models are unable to accurately describe network traffic characteristics, leading to poor prediction performance and low accuracy of prediction problems.…”

Section: A Backgroundmentioning

confidence: 99%

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Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

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Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.

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Section: A Backgroundmentioning

confidence: 99%

“…The control parameter of abandoning food source is randomly assigned within [10,100], optimized parameter is L∈ [2,500], m∈ [1,n/30], τ2∈ [1,n/50], K∈ [2,15], τ1∈[0. 1,3]. n is the number of samples.…”

Section: Sabc (Proposed)mentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

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“…e literature shows many algorithms are used in ANN to optimize the training process. Among them, Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG) are three frequently used algorithms in ANN [25][26][27][28].…”

Section: Various Algorithms In the Literaturementioning

confidence: 99%

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Amaratunga

Wickramasinghe

Perera

et al. 2020

Mathematical Problems in Engineering

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Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

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“…Network traffic is an important parameter to evaluate the running state of a network. It is found to be a nonlinear time series [11] which has the characteristics of time-variability, long-term correlation, selfsimilarity, suddenness and chaos [12]. Therefore, a more accurate and fast response traffic prediction model is much desired to ensure a safe and healthy network situation.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on hybrid network traffic prediction model

Shi

Leau

et al. 2021

IJECE

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Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

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A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

Cited by 21 publications

References 26 publications

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

A comprehensive review on hybrid network traffic prediction model

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