TARFVAE：通过TARFLOW基于VAE实现高效的单步生成时间序列预测
TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
AI总结：
TARFVAE结合Transformer自回归流与VAE，实现高效单步生成时间序列预测，提升预测效率与性能。
摘要：
时间序列数据无处不在，其预测应用从金融到医疗领域均有涉及。除了流行的确定性方法外，生成模型由于在图像合成和视频生成等领域的进步以及其固有的能力，能够提供概率预测，正受到越来越多的关注。然而，现有的生成方法大多涉及递归生成操作或重复去噪步骤，使预测变得繁琐，尤其是长期预测。大多数方法只对相对较短的预测进行了实验，与确定性方法在长期预测中的比较有限，其实际优势尚不明确。本文提出了TARFVAE，一种新的生成框架，结合了基于Transformer的自回归流（TARFLOW）和变分自编码器（VAE），用于高效的单步生成时间序列预测。受复杂架构提取时间序列表示可能不必要的再思考启发，我们向VAE添加了流模块TARFLOW，以促进潜在变量的自发学习，从而促进预测。TARFLOW通过打破高斯假设增强了VAE的后验估计，从而使得潜在空间更加信息丰富。TARFVAE仅使用TARFLOW的正向过程，避免了自回归反向操作，从而确保了快速生成。在生成过程中，它从先验潜在空间中采样，并通过VAE解码器直接生成完整的预测范围。通过简单的MLP模块，TARFVAE在不同预测范围内基准数据集上实现了优于当前最先进确定性和生成模型的性能，同时保持了高效的预测速度，证明了其作为生成时间序列预测高效且有力的解决方案的有效性。
英文摘要：
Time series data is ubiquitous, with forecasting applications spanning from finance to healthcare. Beyond popular deterministic methods, generative models are gaining attention due to advancements in areas like image synthesis and video generation, as well as their inherent ability to provide probabilistic predictions. However, existing generative approaches mostly involve recurrent generative operations or repeated denoising steps, making the prediction laborious, particularly for long-term forecasting. Most of them only conduct experiments for relatively short-term forecasting, with limited comparison to deterministic methods in long-term forecasting, leaving their practical advantages unclear. This paper presents TARFVAE, a novel generative framework that combines the Transformer-based autoregressive flow (TARFLOW) and variational autoencoder (VAE) for efficient one-step generative time series forecasting. Inspired by the rethinking that complex architectures for extracting time series representations might not be necessary, we add a flow module, TARFLOW, to VAE to promote spontaneous learning of latent variables that benefit predictions. TARFLOW enhances VAE's posterior estimation by breaking the Gaussian assumption, thereby enabling a more informative latent space. TARFVAE uses only the forward process of TARFLOW, avoiding autoregressive inverse operations and thus ensuring fast generation. During generation, it samples from the prior latent space and directly generates full-horizon forecasts via the VAE decoder. With simple MLP modules, TARFVAE achieves superior performance over state-of-the-art deterministic and generative models across different forecast horizons on benchmark datasets while maintaining efficient prediction speed, demonstrating its effectiveness as an efficient and powerful solution for generative time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
