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Pattern Matching in Sequential Data Using Reservoir Projections

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Advances in Neural Networks – ISNN 2019 (ISNN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11554))

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Abstract

A relevant problem on data science is to define an efficient and reliable algorithm for finding specific patterns in a given signal. This type of problems often appears in medical applications, biophysical systems, complex systems, financial analysis, and several other domains. Here, we introduce a new model based in the ability of Recurrent Neural Networks (RNNs) for modelling time series. The technique encodes temporal information of the reference signal and the given query in a feature space. This encoding is done using a RNN. In the feature space, we apply similarity techniques for analysing differences among the projected points. The proposed method presents advantages with respect of state of art, it can produce good results using less computational costs. We discuss the proposal over three benchmark datasets.

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Acknowledgements

This work was supported by the projects SP2019/135 and SP2019/141 of the Student Grant System, VSB-Technical University of Ostrava, Czech Republic, and by the Ministry of Education, Youth and Sports from the Specific Research Projects (SP2019/135 and SP2019/141) and by The Technology Agency of the Czech Republic in the frame of the project TN01000024 National Competence Center-Cybernetics and Artificial Intelligence.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Informatics, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Sebastián Basterrech

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  1. Sebastián Basterrech
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Correspondence to Sebastián Basterrech .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Basterrech, S. (2019). Pattern Matching in Sequential Data Using Reservoir Projections. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-22796-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22795-1

  • Online ISBN: 978-3-030-22796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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