## 1 Introduction  
Catches of spiny lobsters (_Panulirus_ spp.) in Mexico amount to 4900 tons in 2018 [1]. The prices of these catches translate to an income of approximately USD 53 million, which is an essential income for maintaining several coastal communities that depend on small-scale fisheries [2, 3]. The area that mainly benefits from this resource is the western coast of the Baja California peninsula, home to the red lobster (_Panulirus interruptus_), a species that accounts for 66% (\(\approx\)3.200 mt) of lobster catches in Mexico. Forty-eight percent of these catches are exported, mainly to China (47%) [1, 4]. The average annual export prices are relatively stable, showing a steady rise from 21 USD/kg to 40 USD/kg from 2006 to 2020 [4]; on an interannual scale, however, catches tend to be highly variable. This fishery has a closed season that lasts from 1 March to 30 September [5], during which the sale and marketing of the product is strictly prohibited. It is marketed as whole live lobster. From an economic perspective, price uncertainty translates into the suboptimal utilization of fishery resources [6, 7]. For this reason, it is essential to have a reliable method of predicting future prices that will improve the decision making of resource users. Having a forecast of the lobster export price with a monthly resolution represents basic information for the marketing strategy of fishers to accrue economic benefits through the better planning of catches during the fishing season. It is possible to implement a scheduled catch during the season for this fishery since it is managed through TURF (Territorial Use Rights for Fishing), allocated to fishing cooperatives according to minimum sizes and quotas. In the study area, this management system is characterized by effective governance, and in 2004 it was the first fishery to be certified by the Marine Stewardship Council [8]. Price forecasting can benefit resource management; however, empirically estimating future prices is a complex issue [9; 10; 11; 12]. As the red lobster is an export product, its price is not only set according to seasonality and catch quantity in a given season but is influenced by the dynamics of international markets [13]. For this reason, price forecasting requires the use of robust analytical tools [14; 15; 16]. Despite the importance of price in decision making related to natural resources, there is little research on the price forecasting of fisheries' resources. Articles related to the price forecasting of pelagic fishes using the autoregressive integrated moving average with exogenous variables (ARIMAX) as a prediction model are reported in the scientific literature [17; 18; 19]. These works found that ARIMAX models fit the prediction of the price pattern, but as expected, the accuracy of the algorithm decreases as the forecast period becomes longer. To overcome the predictive power limitations of ARIMAX, the application of artificial neural networks (ANNs) has been proposed, which generally yield better predictive power [20; 21; 22; 23; 24; 25]. ANNs are models that seek to reproduce the behavior of the human brain using a set of artificial neurons connected to each other to transmit signals [26]. ANNs are a family of powerful algorithms with which complex behaviors can be modeled when calculating a mathematical function. A trigger function is applied to a weighted sum of input values to produce an output value. Moreover, ANNs are non-parametric approximation methods commonly used in the construction of non-linear regression models [27; 28]. For non-linearity, there are univariate autoregressive neural networks (NAR) based on a single data series; for one or more exogenous variables, there are NARX, which explain the dependent variable. ANN predictions tend to perform better than ARIMAX, as ANNs are more efficient at modeling volatility.