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.
Highlights
Rising fishing efforts as well as climate change and their resultant impacts on fisheries stocks are most widely felt by local fisher folks who rely on fishing for their livelihoods
Due to the principal role predictive models play in key management decisions; this work examined the comparative accuracies of different univariate time series approaches in forecasting annual fishery landings
Model Building and Diagnostics The various techniques outlined in the previous section have been applied to model annual fishery production data (Figure 1)
Summary
Rising fishing efforts (and/overexploitation) as well as climate change and their resultant impacts on fisheries stocks are most widely felt by local fisher folks who rely on fishing for their livelihoods. Two different approaches namely deterministic and stochastic models have been developed vis-à-vis forecasting of fisheries landings. Whereas the former is based primarily on the population dynamics theory, stochastic models emanate from the econometric and business literature. Time series approaches such as artificial neural network and Autoregressive Moving Average models have been used in some studies [14]-[16]. Due to the principal role predictive models play in key management decisions; this work examined the comparative accuracies of different univariate time series approaches in forecasting annual fishery landings
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
