论文标题
多变量时间自动编码器，用于预测深层序列的预测性重建
Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences
论文作者
论文摘要
在现实世界中，时间序列序列预测和建模已被证明是一项艰巨的努力。两个关键问题是数据的多维性和形成潜在输出信号的独立维度的相互作用，以及预测模型内部多维时间数据的表示。本文提出了一种多分支深度神经网络方法，通过使用经常性的自动编码器分支对数据窗口的潜在状态矢量表示来解决上述问题，然后将训练有素的潜在矢量表示为模型的预测分支。因此，该模型被称为多元时间自动编码器（MVTAE）。本文中的框架利用了一个合成的多元时间数据集，该数据集包含结合尺寸以创建隐藏的输出目标。
Time series sequence prediction and modelling has proven to be a challenging endeavor in real world datasets. Two key issues are the multi-dimensionality of data and the interaction of independent dimensions forming a latent output signal, as well as the representation of multi-dimensional temporal data inside of a predictive model. This paper proposes a multi-branch deep neural network approach to tackling the aforementioned problems by modelling a latent state vector representation of data windows through the use of a recurrent autoencoder branch and subsequently feeding the trained latent vector representation into a predictor branch of the model. This model is henceforth referred to as Multivariate Temporal Autoencoder (MvTAe). The framework in this paper utilizes a synthetic multivariate temporal dataset which contains dimensions that combine to create a hidden output target.
