Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2018 24th International Conference on Pattern Recognition, ICPR 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 776-781 |
| Number of pages | 6 |
| Volume | 2018-August |
| ISBN (Electronic) | 9781538637883 |
| DOIs | |
| Publication status | Published - 2018 Nov 26 |
| Event | 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China Duration: 2018 Aug 20 → 2018 Aug 24 |
Other
| Other | 24th International Conference on Pattern Recognition, ICPR 2018 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 18/8/20 → 18/8/24 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Sequential Fish Catch Forecasting Using Bayesian State Space Models. / Kokaki, Yuya; Tawara, Naohiro; Kobayashi, Tetsunori; Hashimoto, Kazuo; Ogawa, Tetsuji.
2018 24th International Conference on Pattern Recognition, ICPR 2018. Vol. 2018-August Institute of Electrical and Electronics Engineers Inc., 2018. p. 776-781 8546069.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Sequential Fish Catch Forecasting Using Bayesian State Space Models
AU - Kokaki, Yuya
AU - Tawara, Naohiro
AU - Kobayashi, Tetsunori
AU - Hashimoto, Kazuo
AU - Ogawa, Tetsuji
PY - 2018/11/26
Y1 - 2018/11/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059747873&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059747873&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2018.8546069
DO - 10.1109/ICPR.2018.8546069
M3 - Conference contribution
AN - SCOPUS:85059747873
VL - 2018-August
SP - 776
EP - 781
BT - 2018 24th International Conference on Pattern Recognition, ICPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -