Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. MCFOs extend the choices of likelihood estimators beyond Sequential Monte Carlo in state-of-the-art objectives, possess important properties revealing the factors for the tightness of objectives, and allow for less biased and variant gradient estimates. We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data.

翻译：学习基因变异模型和推断潜在轨迹已证明对时间序列具有挑战性,因为灵活基因变异模型的边际可能性难以捉摸,可以通过替代目标优化加以解决,我们提出蒙特卡洛过滤目标(MMCFOs),这是联合学习参数变异基因变异模型和分解时间序列的适应重要性建议的一系列变异目标。在最先进的目标中,MCFO将可能测算者的选择扩大到定时序列之外,拥有重要属性,揭示了目标的紧凑性因素,并允许进行较少偏差和变差梯度估计。我们证明,拟议的MCFO和梯度估计可导致高效和稳定的模型学习,所学的基因变异模型能够很好地解释数据和重要性建议在各种时间序列数据上更为有效。