Price Forecast for Mexican Red Spiny Lobster (Panulirus spp.) Using Artificial Neural Networks (ANNs)

Abstract

The selling price is one of the essential variables in decision making for fishers regarding the catching of a fishing resource. In the case of the Pacific Mexican lobster fishery, the price uncertainty at the beginning of the season translates into the suboptimal utilization of this resource. This work aims to predict the export price of Mexican red lobster (Panulirus) in a fishing season using demand-related market variables including price, main competitors, main buyers, and product quantities exported/imported in the market. We used the monthly export price from 2006 to 2018 for the main importer, China. As a method for price forecasting, artificial neural networks (ANNs), with and without exogenous variables (NARX, NAR), were used as an autoregressive model, while the same information was analyzed with an ARIMAX model for comparative purposes. It was found that ANNs are a useful tool that yielded better predictive power when forecasting Mexican lobster export prices compared to ARIMAX models. The predictive power was evaluated by comparing the mean square errors (MSE) of 15 models. The MSE of ANNs (73.07) was lower than that of the four ARIMAX models (88.1). It is concluded that neural networks are a valuable tool for accurately predicting prices relative to real values, an aspect of great interest for application in fishery resource management.