Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends

Vanguri, Nagarjun Yadav; Pazhanirajan, S.; Kumar, T. Anil

doi:10.1007/s41315-022-00250-2

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Existing state-of-the-art competitive neural network models, such as Growing Neural Gas (GNG) [1,2], Self-Organizing Neural Network (SONN) [3,4], Adaptive Resonance Theory (ART) [5], however, are designed for static offline data and cannot effectively cope with evolving data streams. There are a few competitive neural network-based methods proposed for time series anomaly detection [6][7][8]. However, they focus on learning the time-varying characteristic via adding adaptive learning strategies, ignoring the evaluation of the computational overhead of improved algorithms for evolving data streams.…”

Section: Introductionmentioning

confidence: 99%

Bayes-Optimized Adaptive Growing Neural Gas Method for Online Anomaly Detection of Industrial Streaming Data

Zhang,

Guo,

Gao

et al. 2024

Applied Sciences

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Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall short of such challenges, and these models may be disabled, suffering from the evolving data distribution. The paper presents a framework for online anomaly detection of data streams, of which the baseline algorithm is the incremental learning method of Growing Neural Gas (GNG). It handles complex and evolving data streams via the proposed model Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG). Firstly, novel learning rate adjustment and neuron addition strategies are designed to enhance the model convergence and data presentation capability. Then, the Bayesian algorithm is adopted to realize the fine-grained search of BOA-GNG-based hyperparameters. Finally, comprehensive studies with six data sets verify the superiority of BOA-GNG in terms of detection accuracy and computational efficiency.

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