Next Article in Journal
Embedding Learning with Triple Trustiness on Noisy Knowledge Graph
Previous Article in Journal
The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design
Open AccessArticle

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

1
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile
2
Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso 2391415, Chile
3
Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1082; https://doi.org/10.3390/e21111082
Received: 26 September 2019 / Revised: 25 October 2019 / Accepted: 1 November 2019 / Published: 5 November 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That is why in this research, a transfer function method with variable coefficients is proposed to forecast monthly disembarkation of anchovies and sardines in northern Chile, taking into account the incidence of large-scale climatic variables on landings. The method uses a least squares procedure and wavelets to expand the coefficients of the transfer function. Linear estimators of the time varying coefficients are proposed, followed by a truncation of the wavelet expansion up to an appropriate scale. Finally, the estimators for the transfer function coefficients are obtained by using the inverse wavelet transformation. Research results suggest that the transfer function models with variable coefficients fit the behavior of the anchovies’ landing with great accuracy, while the use of transfer function models with constant coefficients fits sardines’ landings better. Both fisheries’ landings could be explained to a large extent from the large scale climatic variables. View Full-Text
Keywords: fisheries’ landings; time series forecasting; wavelets fisheries’ landings; time series forecasting; wavelets
Show Figures

Graphical abstract

MDPI and ACS Style

Vivas, E.; Allende-Cid, H.; Salas, R.; Bravo, L. Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables. Entropy 2019, 21, 1082.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop