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Feedforward Factorial Hidden Markov Model

Author

Listed:
  • Zhongxing Peng

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

  • Wei Huang

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

  • Yinghui Zhu

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

Abstract

This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through predefined states, which are derived using an automatic feature filter (AFF). We investigate two variations of FFHMM models that integrate predefined states with the FHMM: the direct FFHMM and the embedded FFHMM. In the direct FFHMM, alterations to one sub-hidden Markov model (HMM) do not affect the others, enabling individual improvements in HMM estimation. On the other hand, the sub-HMM chains within the embedded FFHMM are interconnected, suggesting that adjustments to one HMM chain may enhance the estimations of other HMM chains. Consequently, we propose two algorithms for these FFHMM models to estimate their respective hidden states. Ultimately, experiments conducted on two real-world datasets validate the efficacy of the proposed models and algorithms.

Suggested Citation

  • Zhongxing Peng & Wei Huang & Yinghui Zhu, 2025. "Feedforward Factorial Hidden Markov Model," Mathematics, MDPI, vol. 13(7), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1201-:d:1628661
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    References listed on IDEAS

    as
    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, December.
    2. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
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