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Modelling and Forecasting Monthly Fisheries Catches: Comparison of Regression, Univariate and Multivariate Time Series Methods by K.I. Stergiou; E.D. Christou; G. Petrakis is a Environmental Science article available to read on EtoBox.

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In the present work, seven forecasting techniques were evaluated on the basis of their efficiency to model and provide accurate operational forecasts of the monthly commercial landings of 16 species (or groups of species) in the Hellenic marine waters. The development of operational forecasts was based on the following three general categories of forecasting techniques: (a) deterministic simple or multiple regression models incorporating different exogenous variables (seasonal time-varying regression, TVS; multiple regression models, MREG, incorporating time, number of fishers, wholesale value of catch and climatic variables); (b) univariate time series models (Winter's three parameter exponential smoothing, WES; ARIMA); and (c) multivariate time series techniques (harmonic regression, HREG; dynamic regression, DREG; vector autoregressions, VAR). Fits (for 1964-1987) and forecasts (for 1988-1989) obtained by the different models were compared with each other and with those of two naive methods (NM1 and NM12) and an empirical one (i.e. combination of forecasts, EMP) using 32 different measures of accuracy. The results revealed that the univariate ARIMA, closely followed by the multi

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Author

K.I. Stergiou; E.D. Christou; G. Petrakis

Publisher

Elsevier BV

Published

1997

Language

EN

Field

Environmental Science (Physical Sciences)