Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach

Abstract

In this study the performance of computational neural networks (CNNs) models to forecast 1-month ahead monthly anchovy catches in the north area of Chile considering only anchovy catches in previous months as inputs to the models was analysed. For that purpose several CNN approaches were implemented and compared: (a) typical autoregressive univariate CNN models; (b) a convolution process of the input variables to the CNN model; (c) recurrent neural networks (Elman model); (d) a hybrid methodology combining CNN and ARIMA models. The results obtained in two different external validation phases showed that CNN having inputs of anchovy catches of the 6 previous months hybridised with ARIMA(2,0,0) provided very accurate estimates of the monthly anchovy catches. For this model, the explained variance in the external validation fluctuated between 84% and 87%, the standard error of prediction (SEP, %) was lower than 31% and mean absolute error (MAE) was around 18,000 tonnes. Also, significant results were obtained with recurrent neural networks and seasonal hybrid CNN + ARIMA models. The strong correlation among estimated and observed anchovy catches in the external validation phases suggests that calibrated models captured the general trend of the historical data and therefore these models can be used to carry out an accuracy forecast in the context of a short-medium term time period.