Empirical Modeling of Annual Fishery Landings

Abstract

Forecasting plays an essential role in policy formulation and implementation especially in the management of fisheries resources. In this paper, various techniques of forecasting using time series analysis were evaluated on annual fishery production data. In addition to the Box-Jenkins approach, other methods such as the feed forward neural network and exponential smoothing approaches were also examined. A parsimonious model for each forecasting approach was then selected using penalized likelihoods. The chosen models were then evaluated based on their ability to produce accurate forecasts. Implications of the findings as discussed revealed that no particular method was ideal for modeling all landings. Hence when forecasting fishery landings, it is recommended that different structural approaches be compared before selecting an appropriate one for use.

A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana

“…Self-projecting approaches produce predictions using only time series data of the activity to be forecasted while the latter relies on relationships between the time series to be forecasted and one or more series that influence it. Several models for forecasting time series data have been developed (e.g., exponential, ARIMA, GARCH, and artificial intelligence) and applied to diverse fields of study including medicine over the past decades [ 19 ]. Available literature on malaria forecasting shows that quite a large number of studies rely on the cause-effect approach where covariates such as temperature, vegetation (NDVI), and/or rainfall among others are included in malaria models [ 20 – 24 ].…”

Improving landings forecasts using environmental covariates: A case study on the Indian oil sardine (Sardinella longiceps)

“…The modeling and forecasting of landings using time‐series models have a long history in fisheries research (Cohen & Stone, 1987; Farmer & Froeschke, 2015; Georgakarakos et al, 2006; Hanson et al, 2006; Lawer, 2016; Lloret et al, 2000; Mendelssohn, 1981; Nobel & Sathianandan, 1991; Prista et al, 2011; Stergiou & Christou, 1996), including for oil sardines (Sajna et al, 2019; Srinath, 1998; Venugopalan & Srinath, 1998). These models can be used to identify variables correlated with catch fluctuations and to provide short‐term landings forecasts, which are useful for fishery managers (e.g., Farmer & Froeschke, 2015) and the fishing industry (e.g., Hanson et al, 2006; Schaaf et al, 1975).…”