Title: An innovative kernel-based recursive time-series learning algorithm with applications to improvements of beehive management practices
Authors: Jack Penm
Addresses: School of Finance and Applied Statistics, ANU College of Business and Economics, The Australian National University, Australia
Abstract: In 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: beehive management; innovation; time-series learning algorithms; subset vector autoregressive modelling; honey bees; honey bee foragers; nectar foraging; pollen foraging; honey yield.
International Journal of Innovation and Learning, 2008 Vol.5 No.2, pp.155 - 169
Published online: 22 Jan 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article