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Fuzzy Hidden Markov Chain Based Models for Time-Series Data

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Advances in Computational Intelligence Systems (UKCI 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1454))

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Abstract

The hidden Markov model (HMM) has shown a remarkable capability when dealing with time series data. However, when extended to multiple sequence datasets, the alignment is of central importance. This paper presents a new modelling design framework for pairing HMM. In this framework, an interval type-II fuzzy logic (IT2FL) model is applied for multiple sequence alignment. The inference model can determine which data sequences mostly reflect the current state of the system. Unlike the mathematical computing methods, the model requires less amount of data and leads to a low computation complexity. Results show that the accuracy of the IT2FL model is better than that of an Artificial Neural Network (ANN), especially when the datasets are not large. This model can also be used for real-time multiple time series prediction and monitoring.

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Authors and Affiliations

  1. Department of Advanced Control and System Engineering, The University of Sheffield, Sheffield, UK

    Yihui Tao & Mahdi Mahfouf

Authors
  1. Yihui Tao
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  2. Mahdi Mahfouf
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Correspondence to Yihui Tao .

Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK

    George Panoutsos

  2. Department of Automatic Control and Systems Engineering, Head of the Intelligent Systems Research Laboratory, The University of Sheffield, Sheffield, South Yorkshire, UK

    Mahdi Mahfouf

  3. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, South Yorkshire, UK

    Lyudmila S Mihaylova

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Tao, Y., Mahfouf, M. (2024). Fuzzy Hidden Markov Chain Based Models for Time-Series Data. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_2

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