Pain is usually measured by patient's self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies have explored the feasibility of using physiological autonomic signals to detect the presence of pain. In this study, we focused on continuously estimating experimental heat pain intensity with high temporal resolution from autonomic signals. Specifically, we employed skin conductance deconvolution and point process heart rate variability analysis to continuously evaluate time-varying autonomic parameters, and presented a regression algorithm based on recurrent neural networks.