An innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices

Penm, Jack HW2015-12-071741-8089http://hdl.handle.net/1885/18940In this paper, we propose an innovative kernel-based learning algorithm to sequentially estimate subset vector autoregressive models (including full-order models). To demonstrate the effectiveness of the proposed recursive algorithm, we apply this algorithm to test the direct causal relationships between the population of honeybee foragers and foraging types gathering nectar, pollen or water. We have found that under certain conditions, nectar foraging may be improved by the changes in the proportions of other foraging bees, such as pollen foragers. This suggests that we may be able to predict the optimal conditions at any time to maximise the honey yield of colonies.Keywords: algorithm; apiculture; foraging behavior; modeling; time series analysis; Apis mellifera; Apoidea Beehive management; Innovation; Learning algorithm; Subset vector autoregressive modellingAn innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices200810.1504/IJIL.2008.0167622015-12-07