Mathematical as well as statistical models not only help in
understanding the dynamics of fish populations but also
enables in short-term predictions on abundance. In the
present study, three univariate forecasting techniques viz.,
Holt-Winters, Autoregressive Integrated Moving Average and
Neural Network Autoregression were used to model the
CPUE data series along northeast coast of India. Quarterly
landings data which spans from January 1985 to December
2014 was used for building the model and forecasting. The
accuracy of the forecast was measured using Mean Absolute
Error, Root Mean Square Error and Mean Absolute Percent
Error. Based on the comparison of the model, performance
of Holt-Winter’s model was found to provide more accurate
forecasts than the Autoregressive Integrated Moving Average
and Neural Network Autoregression model. A Holt-Winters
model with smoothing factors α = 0.172, β = 0, γ = 0.529
was found as the suitable model. The presence of seasonality
in the series is evident from gamma value. An ARIMA model
with one non-seasonal moving average term combined with
two seasonal moving average terms was found to be suitable
to model the CPUE series based on the Akaike Information
Criteria. Among the Neural Network Autoregression models
used to fit the CPUE series, a configuration of 13 lagged
inputs and one hidden layer with 7 neurons provided the
best fit
