TimeVAE：用于生成多变量时间序列的变异自动编码器

最近在时间序列领域的合成数据生成方面的工作主要集中在生成对抗网络的使用上。我们提出了一个新的架构，通过使用变异自动编码器（VAEs）来合成时间序列数据。所提出的架构有几个明显的特性：可解释性、编码领域知识的能力和减少训练时间。我们通过对四个多变量数据集的相似性和可预测性来评估数据生成质量。我们用不同大小的训练数据进行实验，以衡量我们的VAE方法以及几种最先进的数据生成方法的数据可用性对生成质量的影响。我们对相似性测试的结果表明，VAE方法能够准确地代表原始数据的时间属性。在使用生成的数据进行下一步预测任务时，拟议的VAE架构始终满足或超过最先进的数据生成方法的性能。虽然降噪可能导致生成的数据偏离原始数据，但我们证明了由此产生的去噪数据可以显著提高使用生成数据进行下一步预测的性能。最后，所提出的架构可以纳入特定领域的时间模式，如多项式趋势和季节性，以提供可解释的输出。这种可解释性在需要模型输出透明度的应用中是非常有利的，或者在用户希望将时间序列模式的先验知识注入生成模型的应用中。

原文题目：TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

原文：Recent work in synthetic data generation in the time-series domain has focused on the use of Generative Adversarial Networks. We propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs). The proposed architecture has several distinct properties: interpretability, ability to encode domain knowledge, and reduced training times. We evaluate data generation quality by similarity and predictability against four multivariate datasets. We experiment with varying sizes of training data to measure the impact of data availability on generation quality for our VAE method as well as several state-of-the-art data generation methods. Our results on similarity tests show that the VAE approach is able to accurately represent the temporal attributes of the original data. On next-step prediction tasks using generated data, the proposed VAE architecture consistently meets or exceeds performance of state-of-the-art data generation methods. While noise reduction may cause the generated data to deviate from original data, we demonstrate the resulting de-noised data can significantly improve performance for next-step prediction using generated data. Finally, the proposed architecture can incorporate domain-specific time-patterns such as polynomial trends and seasonalities to provide interpretable outputs. Such interpretability can be highly advantageous in applications requiring transparency of model outputs or where users desire to inject prior knowledge of time-series patterns into the generative model.