Sequential Fish Catch Forecasting Using Bayesian State Space Models

Yuya Kokaki, Naohiro Tawara, Tetsunori Kobayashi, Kazuo Hashimoto, Tetsuji Ogawa

    研究成果: Conference contribution

    抜粋

    A new state space model suitable for fixed shore net fishing is proposed and successfully applied to daily fish catch forecasting. Accurate prediction of daily fish catches makes it possible to support fishery workers with decision-making for efficient operations. For that purpose, the predictive model should be intuitive to the fishery workers and provide an estimate with a confidence. In the present paper, a fish catch forecasting method is developed using a state space model that emulates the process of fixed shore net fishing. In this method, the parameter estimation and prediction are sequentially performed using the Hamiltonian Monte Carlo method. The experimental comparisons using actual fish catch data and public meteorological information demonstrated that the proposed forecasting system yielded significant reductions in predictive errors over the systems based on decision-trees and legacy state-space models.

    元の言語English
    ホスト出版物のタイトル2018 24th International Conference on Pattern Recognition, ICPR 2018
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ776-781
    ページ数6
    2018-August
    ISBN(電子版)9781538637883
    DOI
    出版物ステータスPublished - 2018 11 26
    イベント24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
    継続期間: 2018 8 202018 8 24

    Other

    Other24th International Conference on Pattern Recognition, ICPR 2018
    China
    Beijing
    期間18/8/2018/8/24

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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  • これを引用

    Kokaki, Y., Tawara, N., Kobayashi, T., Hashimoto, K., & Ogawa, T. (2018). Sequential Fish Catch Forecasting Using Bayesian State Space Models. : 2018 24th International Conference on Pattern Recognition, ICPR 2018 (巻 2018-August, pp. 776-781). [8546069] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8546069