Modelling And Forecasting Small Haplochromine Species (Kambuzi) Production In Malawi - A Stochastic Model Approach
Wales Singini, Emmanuel Kaunda, Victor Kasulo, Wilson Jere
Key words: Forecasting, ARIMA, NBIC, Lake Malombe, Haplochromine, Modelling, production
Abstract: The study aimed at forecasting small Haplochromine species locally known as Kambuzi yield in Malaŵi, based on data on Lake Malombe fish catches during the years from 1976 to 2011. The study considered Autoregressive (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) processes to select the appropriate stochastic model for forecasting small Haplochromine species yield in Lake Malombe. Based on ARIMA (p, d, q) and its components Autocorrelation function (ACF), Partial autocorrelation (PACF), Normalized Bayesian Information Criterion (NBIC), Box - Ljung Q statistics and residuals estimated, ARIMA (0, 1, 1) was selected. Based on the chosen model, it could be predicted that the small Haplochromine species yield would increase to 4,224 tons in 2021 from 93 tons in 1976.
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