Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches |
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| Institution: | 1. Dpto. Biología, Facultad de Ciencias del Mar y Ambientales, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Cádiz, Spain;2. Dpto. de Ciencias Agroforestales, Escuela Politécnica Superior, Universidad de Huelva, Campus Universitario de La Rábida, 21819 Palos de la Frontera, Huelva, Spain;1. Laboratory of Applied Microbiology, Department of Environmental Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India;2. Department of Environmental Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India;1. Division of Gastroenterology and Hepatology, Alameda Health System - Highland Hospital, Oakland, CA, USA;2. Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA, USA;3. Division of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA;1. Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, Japan;2. National Research Institute of Far Seas Fisheries, Fisheries Research Agency, 2-12-4 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, Japan;3. Hachinohe Station, Tohoku National Fisheries Research Institute, Fisheries Research Agency, 25-259 Shimomekurakubo, Same-cho, Hachinohe, Aomori, Japan;4. Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki, Japan;5. School of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu-ku, Shizuoka, Shizuoka, Japan;1. Advanced Center of Research in High Energy Materials (ACRHEM), School of Physics, University of Hyderabad, Telangana 500046, India;2. School of Physics, University of Hyderabad, Hyderabad, Telangana 500046, India;1. State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing 210009, China;2. Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University, Nanjing, 210009 Jiangsu, China |
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| Abstract: | In the present paper, two univariate forecasting techniques were tested to evaluate the short-term CPUE capacity forecast for Pacific halibut, Hippoglossus stenolepis (Pleuronectidae). The first methodology, based on the Box–Jenkins approach (autoregressive integrated moving average models ARIMA models]), assumes a linear relationship between the time series data. The second methodology, using artificial neural network models (ANNs), enables highly non-linear processes to be modelled. The best results from a seasonal ARIMA model indicated that one non-seasonal autoregressive term combined with a non-seasonal moving average term and a seasonal moving average term was suitable to explain a variance level of 32.6% in the validation phase, providing statistically acceptable but insufficiently satisfactory estimations. The configuration of the best ANN model (three autoregressive terms in the input layer and five neurons in the hidden layer) provided a significant improvement in the independent validation phase (91% of the variation explained), indicating a clear non-linear relationship between variables. Modelling of the abundance indices is a useful tool for understanding the dynamics of populations and may enable short-term quantitative recommendations for fisheries management to be made. |
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