The Application of the Neural Network Model Inspired by the Immune System in Financial Time Series Prediction

Mahdi, Asmaa; Hussain, Abir Jaafar; Lisbo, Paulo; Al‐Jumeily, Dhiya

doi:10.1109/dese.2009.39

Cited by 11 publications

(17 citation statements)

References 13 publications

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“…If they are too small, may not move far enough to reach a local minimum [4]. This can be done in two ways: on-line learning or example-by-example, in which the weights out are adjusted after every training pattern; and batch or off-line learning, in which learning (weight adjustment) occurs after all of the training examples have been presented to the network once [5].…”

Section: A Gradient-descent-based Learning Algorithm For the Qrwnnmentioning

confidence: 99%

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A New Quantum Radial Wavelet Neural Network Model Applied to Analysis and Classification of EEG Signals

Taha¹,

Nawar²

2014

IJCA

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In this paper, a new model of multi-level transfer function radial wavelet neural network using quantum computing is achieved. This model is applied to analyze and classify the electroencephalographic (EEG) signals. The independent component analysis (ICA) is used as processing after normalization of these signals. Some features are extracted from the data using the clustering technique (CT). A new factor that combines the accuracy and the time of classification is suggested to evaluate the performance of the proposed model with other previous models. This factor represents the accuracy to time ratio (ATR). The average accuracy of the proposed quantum radial wavelet neural network (QRWNN) model is 90.5619125% at 50 minutes. The ATR value is 1.8112, which shows the superiority of the proposed model.

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Section: A Gradient-descent-based Learning Algorithm For the Qrwnnmentioning

confidence: 99%

“…On other hand quantum intervals can be learned by a minimal output variety of hidden layer neuron based on the same data category of sample data. The output of variance for class is [5] …”

Section: Updating the Quantum Intervalsmentioning

confidence: 99%

A New Quantum Radial Wavelet Neural Network Model Applied to Analysis and Classification of EEG Signals

Taha¹,

Nawar²

2014

IJCA

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“…The new architectures incorporate recurrent links within the structure, the operation of which creates a self-organising layer; inspired by artificial immune system theory. Previous work in this field includes the self-organised multilayer NN inspired by the immune algorithm/SMIA (Mahdi et al, 2009), and the dynamic ridge polynomial higherorder neural network/DRPHONN (Ghazali and Hussain 2009). …”

Section: Dynamic Self-organised Multilayer Network Inspired By the Immentioning

confidence: 99%

Predicting financial time series data using artificial immune system-inspired neural networks

Alaskar

Lamb

Hussain

et al. 2015

IJAISC

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This paper investigates a set of approaches for the prediction of noisy time series data; specifically, the prediction of financial signals. A novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction is presented, combining the properties of both recurrent and self-organised neural networks. In an attempt to overcome inherent stability and convergence problems, the network is derived to ensure that it reaches a unique equilibrium state. The accuracy of the comparative evaluation is enhanced in terms of profit earning; empirical testing used in this work includes normalised mean square error (NMSE) to evaluate forecast fitness and also evaluates predictions against financial metrics to assess profit generation. Extensive simulations for multi-step prediction in stationary and non-stationary time series were performed. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to various solely neural network approaches. These simulations suggest that dynamic immunology-based self-organised neural networks have a better ability to capture the chaotic movement in financial signals.Keywords: financial signals; artificial immune systems; immunology-inspired computing; higher order neural network; time series data.Reference to this paper should be made as follows: Al-askar, H., Lamb, D., Hussain, A.J., Al-Jumeily, D., Randles, M. and Fergus, P. (2015) 'Predicting financial time series data using artificial immune system-inspired neural networks', Int.

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“…Where ∆wojk is the updated weight that connects the hidden and the output units Since most of the published work about financial time series prediction have focused on exchange rate prediction [18,34,48,49], this research has used six exchange rate signals. The foreign exchange market is considered to be the largest market, with more than $1 trillion traded everyday [31,50].…”

mentioning

confidence: 99%

“…The original raw non-stationary signals are transformed into stationary signals before sending them to the neural network, by a transformation technique known as Relative Difference in Percentage of price (RDP) [52]. It is used by many researchers in this field for example [34,48,53,54]. It creates a five-day measure of the relative difference in price data.…”

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confidence: 99%

Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

et al. 2016

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Abstract-This paper presents a novel type of recurrent neural network, the regularized dynamic self-organized neural network inspired by the immune algorithm. The regularization technique is used with the dynamic self-organized multilayer perceptrons network that is inspired by the immune algorithm. The regularization has been addressed to improve the generalization and to solve the over-fitting problem. In this work, the average values of 30 simulations generated from 10 financial time series are examined. The results of the proposed network were compared with the standard dynamic self-organized multilayer perceptrons network inspired by the immune algorithm, the regularized multilayer neural networks and the regularized self-organized neural network inspired by the immune algorithm.The simulation results indicated that the proposed network showed average improvement using the annualized return for all signals of 0.491, 8.1899 and 1.0072 in comparison to the benchmarked networks, respectively.

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The Application of the Neural Network Model Inspired by the Immune System in Financial Time Series Prediction

Cited by 11 publications

References 13 publications

A New Quantum Radial Wavelet Neural Network Model Applied to Analysis and Classification of EEG Signals

A New Quantum Radial Wavelet Neural Network Model Applied to Analysis and Classification of EEG Signals

Predicting financial time series data using artificial immune system-inspired neural networks

Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

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