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Reservoir consisting of diverse dynamical behaviors and its application in time series classification

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Abstract

Time series classification (TSC) has been tackled through a wide range of algorithms. Seminal reservoir computing (s-RC) is composed of a recurrent neural network with random parameters serves as a dynamical memory and is a well-known end-to-end neural networks (NNs) that have been applied to TSC problems. Although the s-RC architecture is suited for dynamic (temporal) data processing, nevertheless choosing the proper design of reservoir plays a crucial role in the efficiency of reservoir computing (RC) in comparison to state-of-the-art fully trainable NNs. In contrast with a large body of researches that has been focused on the aspect of sparsity in the design of RC, in this article, the role of a variety of dynamical behaviors (e.g. stability, periodicity, high-periodicity, and chaos) in RC design is empirically investigated in terms of the richness of the developed dynamical (temporal) representations. Finally, it is shown that RCs with rich dynamical behaviors outperform RCs with a limited spectrum of dynamic behavior in TSC tasks. To evaluate the TSC adaptability of the newly proposed RC framework and state-of-the-art NN-based methods, different experiments on 15 multivariate time series datasets (UCR and UEA datasets) were performed. Our findings divulge that the proposed framework outperforms other RC methods in learning capacity and accuracy and attains classification accuracy comparable with the best fully trainable deep neural networks.

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Authors and Affiliations

  1. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mohammad Modiri, Mohammad Mehdi Ebadzadeh & Mohammad Mehdi Homayounpour

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  1. Mohammad Modiri
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  2. Mohammad Mehdi Ebadzadeh
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  3. Mohammad Mehdi Homayounpour
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Contributions

MM designed and conducted all the experiments, and drafted the manuscript. Dr. MME and Dr. MMH contributed to the guidance of the research, presented valuable comments on the design of the experiments and revised the manuscript. All authors have read and approved this manuscript.

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Correspondence to Mohammad Mehdi Ebadzadeh or Mohammad Mehdi Homayounpour.

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Modiri, M., Ebadzadeh, M.M. & Homayounpour, M.M. Reservoir consisting of diverse dynamical behaviors and its application in time series classification. Int J Data Sci Anal 17, 75–92 (2024). https://doi.org/10.1007/s41060-022-00360-x

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