Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.
翻译:对稀疏多变时间序列(MTS)的预测旨在模拟时间序列未来值的预测数据,因为时间序列的过去不完整,这对许多新兴应用非常重要。然而,大多数现有方法是单独处理多边贸易体系的单个过程,而不是利用多边贸易体系背后的动态分布,从而在宽度高时导致亚最佳结果。为了应对这一挑战,我们提出了一个新颖的基因化模型,用以跟踪潜在群集的过渡,而不是孤立的特征表示,以实现稳健的模型化。其特点是新设计的动态高斯混合分布,它捕捉集群结构的动态,并用于排放时间序列。基因化模型由神经网络进行参数化。结构化推论网络也设计用于进行感化分析。还进一步引入了感应机制,以动态调高斯混合物分布。关于各种真实生命数据集的广泛实验结果显示了我们的方法的有效性。