Multi-event decision making over multivariate time series

We propose a multidimentional time-point model and algorithm to solve multi-event expert query parametric estimation (ME-EQPE) problems over multivariate time series. Our proposed model and algorithm combine the strengths of both domain-knowledge-based and formal-learning-based approaches to learn optimal decision parameters for maximising utility over multivariate time series. More specifically, our approach solves the decision optimisation problems to maximise the utility from multiple decision time points, as well as maintaining an optimality of the learned multiple sets of decision parameters in their respective events during the computations. We show that our approach produces a reasonable forecasting result by using the learned multiple sets of decision parameters.