用潜在空间生成时间序列增强数据，提升预测精度。
L-GTA: Latent Generative Modeling for Time Series Augmentation
- 基于变分自编码器与双向LSTM，学习时序数据的潜在表示。
- 通过可控扰动使增强样本误差降低26%，优于现有方法。
- 适合需要可解释增强的时序预测与异常检测任务。
数据增强在时间序列分析中日益重要，涵盖预测、分类和异常检测。我们提出潜变量生成时序增强模型（L-GTA），基于带双向LSTM主干和时序自注意力的变分自编码器。该模型为每个时间步学习潜在表示，并施加可控扰动如抖动、幅度扭曲或漂移。定义等变目标以促进潜在空间与数据空间变换的一致性，使增强样本具有可预测且可解释的变换特征。在多个真实世界数据集上评估，L-GTA 在下游预测、分布保真度及变换强度可控性方面均优于当前最先进方法（包括TimeGAN、TimeVAE、Diffusion-TS）及直接变换方法。在下游预测中，相比最强生成方法误差降低26%，相对于无增强原始数据减少27%。
原文摘要 · Abstract (English)
Data augmentation is becoming increasingly important across various areas of time series analysis, including forecasting, classification, and anomaly detection. We introduce the Latent Generative Temporal Augmentation (L-GTA) model, a generative approach based on a Variational Autoencoder with a Bi-LSTM backbone and temporal self-attention. The model learns a latent representation for each timestep and applies controlled perturbations such as jittering, magnitude warping, or drift. We define an equivariance objective to further encourage consistency between latent space and data space transformations. As a result, the augmented samples show predictable and interpretable transformation signatures. We evaluate L-GTA on several real-world datasets against SOTA generative methods, including TimeGAN, TimeVAE, and Diffusion-TS, as well as direct transformation approaches. Across experiments on downstream forecasting, distribution fidelity, and controllability of transformation intensity, L-GTA consistently outperforms competing approaches. In downstream forecasting, it reduces prediction error by up to 26% compared to the strongest generative method and 27% relative to using the original data without augmentation.
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