Pubblicazione scientifica

Sequential Fish Catch Forecasting Using Bayesian State Space Models

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità
produzione e cattura

RIASSUNTO

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.

DATA

Data di pubblicazione: 01/08/2018

AUTORI

YUYA KOKAKI
NAOHIRO TAWARA
TETSUNORI KOBAYASHI
KAZUO HASHIMOTO
TETSUJI OGAWA

ENTE DI AFFERENZA

DEPT COMM & COMPUTER ENG WASEDA UNIV TOKYO JAPAN
CTR RSCH STRATEGY WASEDA UNIV TOKYO JAPAN

RIVISTA

2018 24th International Conference on Pattern Recognition (ICPR) (Page(s): 776-781)

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