An adaptive local linear optimized radial basis functional neural network model for financial time series prediction

Patra, A.; Das, Sumanjit; Mishra, Sarojananda; Senapati, Manas Ranjan

doi:10.1007/s00521-015-2039-0

Cited by 33 publications

(11 citation statements)

References 17 publications

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“…Neurons are topological networks in view of biological study and cerebrum reaction mechanism, simulating the step of nerve collide. e ends of a great deal of dendrites accept surface semaphore and convey them to neurons for processing and mixing together and last convey nerves to other neurons or effectors cross over axons [23][24][25][26][27][28]. e topological construction of neurons is shown in Figure 3.…”

Section: Bp Neural Networkmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO-BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA-BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO-BPNN algorithm is superior to GA-BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.

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Section: Bp Neural Networkmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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“…Neural Network (LLRBFNN) model to perform some prediction studies over financial time series [55]. The system has been successful enough on predictions and shown better performances than some other alternative systems reported in the study.…”

Section:  Patra Et Al Employed An Adaptive Local Linear (Optimized)mentioning

confidence: 94%

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Köse

Arslan

2018

Scientia Iranica

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Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

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“…It was reported that the related system is successful enough in the prediction of EEG data [10]. A model of adaptive local linear (optimized) radial basis functional neural network (LLRBFNN) was employed by Patra et al to predict some financial time series [55]. It was reported in that study that LLRBFNN can perform predictions successfully enough and performs better than some other alternative systems that have been considered.…”

Section: A Brief Review Of the Literaturementioning

confidence: 99%

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse

2018

Applied Sciences

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The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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An adaptive local linear optimized radial basis functional neural network model for financial time series prediction

Cited by 33 publications

References 17 publications

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

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