Abstract: A dynamic prediction algorithm based on relationship transfer and reinforcement learning was proposed to alleviate the problem that the dynamic prediction in a large spatiotemporal range fails to obtain an accurate solution. The algorithm adopted a crowd intelligence computing manner with complex workflow models to solve the spatiotemporal data optimization problem. A relationship transfer block was designed to learn the probability of relationship transfer by extracting features from spatiotemporal data. A prediction reinforcement learning block was established along with the time series to process the transition relationship probability in parallel and prioritize the spatiotemporal data according to feature preferences that predict the problem status trend. A deep multi-step iterative strategy optimization was adopted to obtain a reasonable solution. Theoretical analysis and discussion of the convergence and convergence rate of the proposed algorithm were conducted. Experimental results on patent transfer data verified this approach's strengths and demonstrated that the ranking accuracy could be significantly improved by applying the relationship transfer block and prediction reinforcement learning block.

Key words: spatiotemporal data, complex workflow, relationship transfer, feature learning, reinforcement learning